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2026, 39: 100040.
doi: 10.1016/j.cjme.2025.100040
Abstract:
In this study, the shell structure of olives in nature was modeled, and a high-porosity bionic olive body-centered cubic structure (BCCO) with reinforcement structures of circular support (BCCR) and triangular support (BCCT) with excellent mechanical properties was designed and prepared using selective laser melting technology. The surface morphology, deformation behavior, and energy absorption of BCCO were compared with those of the equivalent uniform body-centered cubic structure (BCC) and analyzed through quasi-static compression experiments and finite element analysis. The olive-shaped structure showed optimal load resistance when the radius of curvature was equal to the edge length of the lattice structure, and outperformed with a larger curvature than with a smaller curvature. With the added support structure, the energy absorption of the BCCR increased by 144.44 % compared with that of the conventional BCC structure. The newly designed olive bionic structure has considerable potential for applications in various fields, such as aerospace and medical devices.
In this study, the shell structure of olives in nature was modeled, and a high-porosity bionic olive body-centered cubic structure (BCCO) with reinforcement structures of circular support (BCCR) and triangular support (BCCT) with excellent mechanical properties was designed and prepared using selective laser melting technology. The surface morphology, deformation behavior, and energy absorption of BCCO were compared with those of the equivalent uniform body-centered cubic structure (BCC) and analyzed through quasi-static compression experiments and finite element analysis. The olive-shaped structure showed optimal load resistance when the radius of curvature was equal to the edge length of the lattice structure, and outperformed with a larger curvature than with a smaller curvature. With the added support structure, the energy absorption of the BCCR increased by 144.44 % compared with that of the conventional BCC structure. The newly designed olive bionic structure has considerable potential for applications in various fields, such as aerospace and medical devices.
2026, 39: 100041.
doi: 10.1016/j.cjme.2025.100041
Abstract:
Three-beam wire-feed laser cladding, which generates a uniform energy distribution with a wire vertically fed into the molten pool, is a promising additive manufacturing technology. In this study, an experimental investigation and a statistical analysis of Ti-6Al-4V wire cladding using three-beam laser coaxial wire-feed cladding technology coupled with a 2 kW continuous fiber laser were carried out. The influences of the main parameters, including the laser power, wire feeding speed, and laser scanning speed, on the cladding geometry and process were investigated. The prediction models correlating the process parameters and clad geometry were developed via the response surface methodology (RSM). The models were checked using analysis of variance (ANOVA). Through optimization, the optimal parameters were achieved for the required clad with a width-to-height ratio of 5:1. A high-speed camera was used to investigate the cladding process under various process parameters. The laser power positively affected the widths of the molten pool and cladding layer. The molten pool and clad heights decreased with increases in laser power and scanning speed. Fine acicular martensite grains in the colony and basket-weave distributions were predominant in the cross-section of the cladding layer. The macrostructure investigation showed that the widths of columnar prior-β grains decreased with the increase in laser scanning speed.
Three-beam wire-feed laser cladding, which generates a uniform energy distribution with a wire vertically fed into the molten pool, is a promising additive manufacturing technology. In this study, an experimental investigation and a statistical analysis of Ti-6Al-4V wire cladding using three-beam laser coaxial wire-feed cladding technology coupled with a 2 kW continuous fiber laser were carried out. The influences of the main parameters, including the laser power, wire feeding speed, and laser scanning speed, on the cladding geometry and process were investigated. The prediction models correlating the process parameters and clad geometry were developed via the response surface methodology (RSM). The models were checked using analysis of variance (ANOVA). Through optimization, the optimal parameters were achieved for the required clad with a width-to-height ratio of 5:1. A high-speed camera was used to investigate the cladding process under various process parameters. The laser power positively affected the widths of the molten pool and cladding layer. The molten pool and clad heights decreased with increases in laser power and scanning speed. Fine acicular martensite grains in the colony and basket-weave distributions were predominant in the cross-section of the cladding layer. The macrostructure investigation showed that the widths of columnar prior-β grains decreased with the increase in laser scanning speed.
2026, 39: 100008.
doi: 10.1016/j.cjme.2025.100008
Abstract:
Powder paving is an intermediate process of selective laser sintering (SLS). The dimensional accuracy and mechanical properties of sintered components are directly affected by the quality of the powder paving process, which is closely related to the flow characteristics of the powder and the process parameters of powder paving. This study investigated the simulation and optimization of the nylon powder paving in SLS by combining a discrete-element-method numerical simulation with a process test. A dynamic model was established to describe the flow and paving process of nylon powder at a preheating temperature considering mesoscopic van der Waals and electrostatic forces. The effects of the physical parameters and ambient temperature on the flow characteristics of nylon powder were analyzed, and the intrinsic relationship between the physical parameters of nylon powder, the process parameters of powder paving, and the quality of the powder paving were explored. A multi-objective regression model of the quality of powder paving was established using the response surface methodology, and a genetic algorithm was adopted to optimize the quality of the powder paving. A scientific and intelligent database of the nylon powder paving process in SLS was constructed by matching the process parameters of powder paving and physical parameters of the nylon powder, and the level of the SLS process was improved.
Powder paving is an intermediate process of selective laser sintering (SLS). The dimensional accuracy and mechanical properties of sintered components are directly affected by the quality of the powder paving process, which is closely related to the flow characteristics of the powder and the process parameters of powder paving. This study investigated the simulation and optimization of the nylon powder paving in SLS by combining a discrete-element-method numerical simulation with a process test. A dynamic model was established to describe the flow and paving process of nylon powder at a preheating temperature considering mesoscopic van der Waals and electrostatic forces. The effects of the physical parameters and ambient temperature on the flow characteristics of nylon powder were analyzed, and the intrinsic relationship between the physical parameters of nylon powder, the process parameters of powder paving, and the quality of the powder paving were explored. A multi-objective regression model of the quality of powder paving was established using the response surface methodology, and a genetic algorithm was adopted to optimize the quality of the powder paving. A scientific and intelligent database of the nylon powder paving process in SLS was constructed by matching the process parameters of powder paving and physical parameters of the nylon powder, and the level of the SLS process was improved.
2026, 39: 100013.
doi: 10.1016/j.cjme.2025.100013
Abstract:
Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments, whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes. However, if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool, traditional rotary-turning machining methods are not applicable, and advanced computerized numerical control (CNC) machining methods, such as the slow-tool-servo method, must be implemented. This article proposes a non-conventional offset (NCO) fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts. This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount. NCO fabrication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic surfaces. In addition, corresponding solutions for other types of aspherical surfaces are proposed using the NCO method. The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared. A specific measurement scheme for the NCO method is presented, and the experimental results indicate that the PV and RMS form errors are 0.658 µm and 60 nm, respectively. This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.
Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments, whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes. However, if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool, traditional rotary-turning machining methods are not applicable, and advanced computerized numerical control (CNC) machining methods, such as the slow-tool-servo method, must be implemented. This article proposes a non-conventional offset (NCO) fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts. This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount. NCO fabrication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic surfaces. In addition, corresponding solutions for other types of aspherical surfaces are proposed using the NCO method. The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared. A specific measurement scheme for the NCO method is presented, and the experimental results indicate that the PV and RMS form errors are 0.658 µm and 60 nm, respectively. This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.
2026, 39: 100006.
doi: 10.1016/j.cjme.2025.100006
Abstract:
A full-face rock tunnel boring machine (TBM) has two main parameters: cutterhead thrust and cutterhead torque. Cutterhead thrust can be predicted numerically using different methods, such as the rated thrust of the disc cutter, the indentation test of the disc cutter, a linear breaking experiment, or applying statistical models based on field data. However, cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research. Consequently, we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking. We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine, shoveling and transporting ballast torque, rolling resistance torque, lateral friction resistance torque, and disc cutter lateral imbalance torque, and established the corresponding theory. Through practical engineering verification, we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51 %. If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered, the relative error between the calculated value of cutterhead torque and the actual value is 7.48 %. Therefore, the accuracy of the established cutterhead torque theory is 6.97 % higher than that of the disc cutter linear rolling crushing rock test theory. The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.
A full-face rock tunnel boring machine (TBM) has two main parameters: cutterhead thrust and cutterhead torque. Cutterhead thrust can be predicted numerically using different methods, such as the rated thrust of the disc cutter, the indentation test of the disc cutter, a linear breaking experiment, or applying statistical models based on field data. However, cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research. Consequently, we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking. We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine, shoveling and transporting ballast torque, rolling resistance torque, lateral friction resistance torque, and disc cutter lateral imbalance torque, and established the corresponding theory. Through practical engineering verification, we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51 %. If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered, the relative error between the calculated value of cutterhead torque and the actual value is 7.48 %. Therefore, the accuracy of the established cutterhead torque theory is 6.97 % higher than that of the disc cutter linear rolling crushing rock test theory. The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.
2026, 39: 100029.
doi: 10.1016/j.cjme.2025.100029
Abstract:
Nickel-based single-crystal superalloy DD98M is widely used in high-temperature components such as aeroengines and gas turbines. Since it has only one crystal grain, the theory of slip deformation along the grain boundary of polycrystalline material is not suitable for the machining of a single crystal part. Therefore, micro-drilling of nickel-based single crystal superalloy still faces problems such as unclear cutting formation mechanism and unclear surface/subsurface damage mechanism. In this paper, the formation mechanism and morphological characteristics of chips and burrs were studied by a single-factor experiment, and the plastic deformation rule and damage mechanism were investigated, combined with the changes of subsurface structure and grain type. Finally, the influence of the law and reason of tool wear condition on the hole wall and the drilled subsurface is analyzed. The experimental results indicate that drill chips mainly exhibit three morphologies. Their free surfaces feature a serrated appearance, while the contact surfaces are smooth. The entrance burrs are mainly flanging burrs. With the increase of spindle speed, the burr height decreases from 49.38 to 9.39 μm. As the feed speed increases, the burr height increases from 6.50 to 63.87 μm. The drilled subsurface can be divided into a white layer region, a plastic deformation region, and the matrix according to the microstructural change. As the depth from the machined surface increases, the degree of plastic deformation of the material decreases, the grain size gradually reduces, and the dislocation density decreases. Stacking fault and twinning mostly occur in the high-plastic deformation region, and recrystallization occurs on the machined surface. As the drilling length increases, the degree of tool wear increases, and the adhesion and ablation area on the hole wall surface increase. Moreover, the thickness of the white layer increases from 0 to 8.75 μm, and the thickness of the plastic deformation layer increases from 1.28 to 11.31 μm. The study has significant theoretical and practical implications for the efficient and low-damage machining of micro-holes in the nickel-based single crystal superalloy.
Nickel-based single-crystal superalloy DD98M is widely used in high-temperature components such as aeroengines and gas turbines. Since it has only one crystal grain, the theory of slip deformation along the grain boundary of polycrystalline material is not suitable for the machining of a single crystal part. Therefore, micro-drilling of nickel-based single crystal superalloy still faces problems such as unclear cutting formation mechanism and unclear surface/subsurface damage mechanism. In this paper, the formation mechanism and morphological characteristics of chips and burrs were studied by a single-factor experiment, and the plastic deformation rule and damage mechanism were investigated, combined with the changes of subsurface structure and grain type. Finally, the influence of the law and reason of tool wear condition on the hole wall and the drilled subsurface is analyzed. The experimental results indicate that drill chips mainly exhibit three morphologies. Their free surfaces feature a serrated appearance, while the contact surfaces are smooth. The entrance burrs are mainly flanging burrs. With the increase of spindle speed, the burr height decreases from 49.38 to 9.39 μm. As the feed speed increases, the burr height increases from 6.50 to 63.87 μm. The drilled subsurface can be divided into a white layer region, a plastic deformation region, and the matrix according to the microstructural change. As the depth from the machined surface increases, the degree of plastic deformation of the material decreases, the grain size gradually reduces, and the dislocation density decreases. Stacking fault and twinning mostly occur in the high-plastic deformation region, and recrystallization occurs on the machined surface. As the drilling length increases, the degree of tool wear increases, and the adhesion and ablation area on the hole wall surface increase. Moreover, the thickness of the white layer increases from 0 to 8.75 μm, and the thickness of the plastic deformation layer increases from 1.28 to 11.31 μm. The study has significant theoretical and practical implications for the efficient and low-damage machining of micro-holes in the nickel-based single crystal superalloy.
2026, 39: 100030.
doi: 10.1016/j.cjme.2025.100030
Abstract:
High entropy alloy attracts widespread attention due to its excellent mechanical properties. It becomes a new type of alloy material with high application potential, but the grinding performance of High entropy alloy receives little attention. This paper conducts grinding simulation and surface grinding experiments on FeCoCrNi high entropy and alloys to analyze the grinding removal mechanism of the FeCoCrNi-based High entropy alloy. We also discuss the influence of grinding parameters, element types, element content and forming methods on grinding force and sub-surface plastic deformation after grinding. The simulation and experimental results show that as the increase of grinding depth, both tangential grinding force and normal grinding force increase, and the thickness of sub-surface plastic deformation layer decreases. With the increase of grinding speed, both tangential grinding force and normal grinding force decrease, and the thickness of sub-surface plastic deformation layer caused by grinding process shows a trend of gradual decrease. Under the same processing parameters, the normal grinding force is greater than the tangential grinding force. In FeCoCrNi series high entropy alloys, the grinding force and subsurface plastic deformation layer thickness of high entropy alloys increased with the addition in Ti content. The grinding force and plastic deformation formed by adding Ti element are greater than those formed by adding Al element, and High entropy alloys prepared using laser cladding method exhibit greater grinding force and plastic deformation than those prepared using selective laser melting method. The research results provide theoretical reference and experimental basis for high-quality grinding of high entropy alloys, which may be helpful for the design and manufacturing of high entropy alloy parts.
High entropy alloy attracts widespread attention due to its excellent mechanical properties. It becomes a new type of alloy material with high application potential, but the grinding performance of High entropy alloy receives little attention. This paper conducts grinding simulation and surface grinding experiments on FeCoCrNi high entropy and alloys to analyze the grinding removal mechanism of the FeCoCrNi-based High entropy alloy. We also discuss the influence of grinding parameters, element types, element content and forming methods on grinding force and sub-surface plastic deformation after grinding. The simulation and experimental results show that as the increase of grinding depth, both tangential grinding force and normal grinding force increase, and the thickness of sub-surface plastic deformation layer decreases. With the increase of grinding speed, both tangential grinding force and normal grinding force decrease, and the thickness of sub-surface plastic deformation layer caused by grinding process shows a trend of gradual decrease. Under the same processing parameters, the normal grinding force is greater than the tangential grinding force. In FeCoCrNi series high entropy alloys, the grinding force and subsurface plastic deformation layer thickness of high entropy alloys increased with the addition in Ti content. The grinding force and plastic deformation formed by adding Ti element are greater than those formed by adding Al element, and High entropy alloys prepared using laser cladding method exhibit greater grinding force and plastic deformation than those prepared using selective laser melting method. The research results provide theoretical reference and experimental basis for high-quality grinding of high entropy alloys, which may be helpful for the design and manufacturing of high entropy alloy parts.
2026, 39: 100025.
doi: 10.1016/j.cjme.2025.100025
Abstract:
Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method, large internal power loss, and poor wearable performance, which seriously affect the rehabilitation ability of these robots. Therefore, this study proposes a variable stiffness human–computer interaction contact unit module (VSHCUM) based on the granular jamming mechanism. It is characterized by a double-layer chamber structure: the inner layer is a granular chamber, and the outer layer is an air chamber. The interaction force is transmitted by embedding a rigid support in the inner layer. Unlike the common flexible-belt interactive contact unit, when the exoskeleton is bound to the patient's limb, VSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure. Simultaneously, by adjusting the vacuum level of the granular chamber, the stiffness of the interactive contact unit can be adjusted by a factor of more than five, and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.
Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method, large internal power loss, and poor wearable performance, which seriously affect the rehabilitation ability of these robots. Therefore, this study proposes a variable stiffness human–computer interaction contact unit module (VSHCUM) based on the granular jamming mechanism. It is characterized by a double-layer chamber structure: the inner layer is a granular chamber, and the outer layer is an air chamber. The interaction force is transmitted by embedding a rigid support in the inner layer. Unlike the common flexible-belt interactive contact unit, when the exoskeleton is bound to the patient's limb, VSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure. Simultaneously, by adjusting the vacuum level of the granular chamber, the stiffness of the interactive contact unit can be adjusted by a factor of more than five, and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.
2026, 39: 100032.
doi: 10.1016/j.cjme.2025.100032
Abstract:
How to substitute the human operator with a robot in various assembly tasks has to be taken into full consideration in intelligent manufacturing. Autonomous robotic assembly not only brings with high working efficiency, better product quality and low labor cost, but also helps relieve the increasingly severe problem of population aging. However, numerous existing challenges still prevent its wide applications when a robot is assigned to finish general tasks in unstructured environment. In order to provide a fundamental understanding of the various problems involved in robotic assembly, this paper carries out a review on its recent progress and challenges with 5 key technologies focused on: perception, end-effectors, control methods, learning methods and performance evaluation. Main works in these fields are reviewed and their characteristics are analyzed while typical assembly scenarios are covered. The challenges and future directions in robotic assembly are also discussed on precise perception, robotic hand, error recovery and collaborative robot. In addition to providing a systematic summarization of the required key technologies, this work is aimed at motivating more potential researches in the community of robotics, artificial intelligence, and automation engineering.
How to substitute the human operator with a robot in various assembly tasks has to be taken into full consideration in intelligent manufacturing. Autonomous robotic assembly not only brings with high working efficiency, better product quality and low labor cost, but also helps relieve the increasingly severe problem of population aging. However, numerous existing challenges still prevent its wide applications when a robot is assigned to finish general tasks in unstructured environment. In order to provide a fundamental understanding of the various problems involved in robotic assembly, this paper carries out a review on its recent progress and challenges with 5 key technologies focused on: perception, end-effectors, control methods, learning methods and performance evaluation. Main works in these fields are reviewed and their characteristics are analyzed while typical assembly scenarios are covered. The challenges and future directions in robotic assembly are also discussed on precise perception, robotic hand, error recovery and collaborative robot. In addition to providing a systematic summarization of the required key technologies, this work is aimed at motivating more potential researches in the community of robotics, artificial intelligence, and automation engineering.
2026, 39: 100022.
doi: 10.1016/j.cjme.2025.100022
Abstract:
Soft actuators based on cellulose with highly electro-responsive properties have attracted significant attention in the fields of wearable devices, medical and healthcare devices, soft robots, and human-computer interactions. However, existing cellulose-based soft actuators still need to be improved in terms of actuation displacement, bending strain, and driving frequency. Herein, we report a highly responsive ionic actuator using carboxylated cellulose nanofibers from wood pulp (CNFp), graphene nanosheets (GN), and ionic liquids (IL). The CNFp-IL-GN actuator exhibited a large specific capacitance of 749.11 mF/cm2 under a 25 mV/s scan rate, a large mechanical displacement (25 mm peak-to-peak) under 2.0 V at 0.1 Hz, a broad actuation frequency (0.1 to 10 Hz), and long working stability. Furthermore, bioinspired applications, including bionic dragonflies and artificial soft-touch fingers, have been demonstrated. These results demonstrate that the proposed actuator is a significant method for advancing soft actuators, artificial muscles, and bioinspired robots.
Soft actuators based on cellulose with highly electro-responsive properties have attracted significant attention in the fields of wearable devices, medical and healthcare devices, soft robots, and human-computer interactions. However, existing cellulose-based soft actuators still need to be improved in terms of actuation displacement, bending strain, and driving frequency. Herein, we report a highly responsive ionic actuator using carboxylated cellulose nanofibers from wood pulp (CNFp), graphene nanosheets (GN), and ionic liquids (IL). The CNFp-IL-GN actuator exhibited a large specific capacitance of 749.11 mF/cm2 under a 25 mV/s scan rate, a large mechanical displacement (25 mm peak-to-peak) under 2.0 V at 0.1 Hz, a broad actuation frequency (0.1 to 10 Hz), and long working stability. Furthermore, bioinspired applications, including bionic dragonflies and artificial soft-touch fingers, have been demonstrated. These results demonstrate that the proposed actuator is a significant method for advancing soft actuators, artificial muscles, and bioinspired robots.
2026, 39: 100090.
doi: 10.1016/j.cjme.2025.100090
Abstract:
Parallel machining robot is a new type of robotized equipment for high-efficiency machining structural components with complex geometries. Terminal rigidity is of great importance index for such type of equipment, which affects their load capacity and working accuracy. Before a parallel machining robot can be used for heavy-load and high-efficiency machining, its terminal rigidity should be evaluated systematically. The present study is to quantitatively reveal the stiffness properties of a previously invented Z4 redundantly actuated parallel machining robot (RAPMR). For this purpose, two critical issues, i.e., stiffness modelling and index construction, are clarified to carry out stiffness evaluation of the Z4 RAPMR. Firstly, drawing on the screw theory, a semi-analytic stiffness model of the proposed RAPMR is established at a component level. Secondly, a set of virtual work-based stiffness indices is constructed to evaluate the terminal rigidity of parallel robots. Those indices have a consistent physical unit in describing linear and angular terminal rigidity. With these indices, the local and the global stiffness performance of the Z4 RAPMR are predicted. Thirdly, a laboratory prototype of the proposed RAPMR is fabricated. And the experimental test is performed to verify the correctness of the established stiffness model. The present work is expected to provide fundamental information for further light-weight design and rigidity enhancement.
Parallel machining robot is a new type of robotized equipment for high-efficiency machining structural components with complex geometries. Terminal rigidity is of great importance index for such type of equipment, which affects their load capacity and working accuracy. Before a parallel machining robot can be used for heavy-load and high-efficiency machining, its terminal rigidity should be evaluated systematically. The present study is to quantitatively reveal the stiffness properties of a previously invented Z4 redundantly actuated parallel machining robot (RAPMR). For this purpose, two critical issues, i.e., stiffness modelling and index construction, are clarified to carry out stiffness evaluation of the Z4 RAPMR. Firstly, drawing on the screw theory, a semi-analytic stiffness model of the proposed RAPMR is established at a component level. Secondly, a set of virtual work-based stiffness indices is constructed to evaluate the terminal rigidity of parallel robots. Those indices have a consistent physical unit in describing linear and angular terminal rigidity. With these indices, the local and the global stiffness performance of the Z4 RAPMR are predicted. Thirdly, a laboratory prototype of the proposed RAPMR is fabricated. And the experimental test is performed to verify the correctness of the established stiffness model. The present work is expected to provide fundamental information for further light-weight design and rigidity enhancement.
2026, 39: 100034.
doi: 10.1016/j.cjme.2025.100034
Abstract:
To improve the inadequate Infiltration performance during the process of large arc length grinding, this study proposes a novel minimum quantity lubrication (MQL) grinding method based on magnetic traction nano-lubrication (MTN). By utilizing magnetic fields to enhance lubricant wettability in the grinding zone, the proposed approach improves friction-reduction and anti-wear performance in high-temperature and high-friction environments. A simulated grinding platform was established to investigate the tribological behavior of MTN through systematic friction and wear experiments. First, a novel Fe3O4/graphene magnetic nano-lubricant was synthesized, and the influence of magnetic field strength on its viscosity was investigated. Subsequently, an experimental validation study of the magnetic nanolubricant was conducted, comparing the properties of composite magnetic nanoparticles at different concentrations. Results showed that the friction coefficient curve of the hybrid nano-lubricant was significantly smoother, abrasion mark width was substantially reduced, and surface adhesion was markedly improved. Finally, an optimization study on the ratio of Fe3O4/GR was conducted to achieve optimal performance and economic efficiency. At a 2:1 Fe3O4/GR ratio, the lubricant demonstrated the lowest average friction coefficient (0.32), the smallest wear area (6146 μm2), and the best surface roughness (1.64 μm). This method offers a promising strategy and experimental basis for optimizing lubrication technology in precision machining.
To improve the inadequate Infiltration performance during the process of large arc length grinding, this study proposes a novel minimum quantity lubrication (MQL) grinding method based on magnetic traction nano-lubrication (MTN). By utilizing magnetic fields to enhance lubricant wettability in the grinding zone, the proposed approach improves friction-reduction and anti-wear performance in high-temperature and high-friction environments. A simulated grinding platform was established to investigate the tribological behavior of MTN through systematic friction and wear experiments. First, a novel Fe3O4/graphene magnetic nano-lubricant was synthesized, and the influence of magnetic field strength on its viscosity was investigated. Subsequently, an experimental validation study of the magnetic nanolubricant was conducted, comparing the properties of composite magnetic nanoparticles at different concentrations. Results showed that the friction coefficient curve of the hybrid nano-lubricant was significantly smoother, abrasion mark width was substantially reduced, and surface adhesion was markedly improved. Finally, an optimization study on the ratio of Fe3O4/GR was conducted to achieve optimal performance and economic efficiency. At a 2:1 Fe3O4/GR ratio, the lubricant demonstrated the lowest average friction coefficient (0.32), the smallest wear area (6146 μm2), and the best surface roughness (1.64 μm). This method offers a promising strategy and experimental basis for optimizing lubrication technology in precision machining.
2026, 39: 100047.
doi: 10.1016/j.cjme.2025.100047
Abstract:
The alcohol-based electrolyte exhibits excellent electropolishing properties for titanium alloys. However, its polishing effectiveness diminishes and the ability to polish is weakened or even lost after a certain duration of electropolishing. Consequently, there is a low reuse rate for these electrolytes, significantly limiting their efficiency in electropolishing. In light of this issue, the current study conducted experiments using different electrochemical dissolution times on titanium alloy immersed in NaCl-ethylene glycol electrolytes to explore the main reasons for the failure of the electrolyte. Furthermore, a novel method was proposed to restore the electropolishing ability of expired NaCl-ethylene glycol electrolyte. Subsequently, the titanium alloy was electropolished with recycled alcohol-based electrolyte, and a favorable surface quality was obtained. By this method, the surface roughness Ra of the polished titanium alloy could be improved from Ra 0.498 μm of the expired electrolyte to Ra 0.136 μm of the recyclable electrolyte.
The alcohol-based electrolyte exhibits excellent electropolishing properties for titanium alloys. However, its polishing effectiveness diminishes and the ability to polish is weakened or even lost after a certain duration of electropolishing. Consequently, there is a low reuse rate for these electrolytes, significantly limiting their efficiency in electropolishing. In light of this issue, the current study conducted experiments using different electrochemical dissolution times on titanium alloy immersed in NaCl-ethylene glycol electrolytes to explore the main reasons for the failure of the electrolyte. Furthermore, a novel method was proposed to restore the electropolishing ability of expired NaCl-ethylene glycol electrolyte. Subsequently, the titanium alloy was electropolished with recycled alcohol-based electrolyte, and a favorable surface quality was obtained. By this method, the surface roughness Ra of the polished titanium alloy could be improved from Ra 0.498 μm of the expired electrolyte to Ra 0.136 μm of the recyclable electrolyte.
2026, 39: 100043.
doi: 10.1016/j.cjme.2025.100043
Abstract:
The surfaces of brittle materials are susceptible to defects such as scratches, cracks, and chipping during conventional grinding processes, which significantly compromise surface quality and service performance. A flexible ball-end body-armor-like abrasive tool (BAAT) can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force, thereby achieving nano-level surface roughness and ultra-smooth mirror finishes. However, the surface contact mechanism, pressure distribution pattern, and grinding force behavior between BAAT and workpiece remain inadequately understood. This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas, drawing on the theories of elastohydrodynamic lubrication, non-Newtonian fluid dynamics, and material mechanics. A high-shear low-pressure grinding force model, which incorporates elastohydrodynamic liquid film thickness and abrasive grain size, was developed. The effects of the main grinding parameters (normal load, spindle rotational speed, and abrasive grain size) on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system. The experimental results indicated that the relative error between the predicted and experimental values was 10.74 %, thereby confirming the accuracy of the grinding force model. This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.
The surfaces of brittle materials are susceptible to defects such as scratches, cracks, and chipping during conventional grinding processes, which significantly compromise surface quality and service performance. A flexible ball-end body-armor-like abrasive tool (BAAT) can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force, thereby achieving nano-level surface roughness and ultra-smooth mirror finishes. However, the surface contact mechanism, pressure distribution pattern, and grinding force behavior between BAAT and workpiece remain inadequately understood. This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas, drawing on the theories of elastohydrodynamic lubrication, non-Newtonian fluid dynamics, and material mechanics. A high-shear low-pressure grinding force model, which incorporates elastohydrodynamic liquid film thickness and abrasive grain size, was developed. The effects of the main grinding parameters (normal load, spindle rotational speed, and abrasive grain size) on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system. The experimental results indicated that the relative error between the predicted and experimental values was 10.74 %, thereby confirming the accuracy of the grinding force model. This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.
2026, 39: 100019.
doi: 10.1016/j.cjme.2025.100019
Abstract:
Micron-sized graphene sheets have been introduced as additives to enhance the lubricating capabilities of water. The tribological characteristics of the lubricants after preparation and storage for 6 months were systematically analyzed. Results indicated that the friction coefficient and wear volume of the tribo-pair were reduced through the incorporation of a certain concentration of graphene sheets, and also have long-term storage stability. Notably, under the experimental conditions, a 0.2% mass concentration of graphene in the aqueous lubricant exhibited exceptional tribological performance and long-term storage stability, achieving an 80% reduction in friction coefficient and a 78% decrease in wear volume with a 14000‐cycle friction test. Wear morphology analysis indicated that after adding graphene sheets to the aqueous solution, micro-plastic deformation occurs on the worn surface of the steel plate. The wear profile of the GCr15 counter ball changes from a circular profile to a rectangular-like profile. The main reason is that the graphene sheets in the aqueous solution can enter the contact interface during the friction process, hindering direct contact between the friction pair. The study provides a simple method to improve the tribological properties of aqueous solutions stably for engineering applications.
Micron-sized graphene sheets have been introduced as additives to enhance the lubricating capabilities of water. The tribological characteristics of the lubricants after preparation and storage for 6 months were systematically analyzed. Results indicated that the friction coefficient and wear volume of the tribo-pair were reduced through the incorporation of a certain concentration of graphene sheets, and also have long-term storage stability. Notably, under the experimental conditions, a 0.2% mass concentration of graphene in the aqueous lubricant exhibited exceptional tribological performance and long-term storage stability, achieving an 80% reduction in friction coefficient and a 78% decrease in wear volume with a 14000‐cycle friction test. Wear morphology analysis indicated that after adding graphene sheets to the aqueous solution, micro-plastic deformation occurs on the worn surface of the steel plate. The wear profile of the GCr15 counter ball changes from a circular profile to a rectangular-like profile. The main reason is that the graphene sheets in the aqueous solution can enter the contact interface during the friction process, hindering direct contact between the friction pair. The study provides a simple method to improve the tribological properties of aqueous solutions stably for engineering applications.
2026, 39: 100011.
doi: 10.1016/j.cjme.2025.100011
Abstract:
While microwave (MW) discharge technology has been developed to address the challenges inherent in sharpening metal-bonded diamond grinding wheels (MD-GW), the surface morphology and grinding performance characteristics of wheels processed through this method remain insufficiently characterized and warrant further investigation. This study employed an in-situ experimental setup to analyze MD-GW sharpened through MW discharge, with a focus on abrasive damage, grit protrusion height and uniformity, the number of effective abrasives, chip space, and bond morphology. The grinding performance of MW-sharpened MD-GW was assessed based on dynamic grinding ratios and surface quality in zirconia grinding experiments, using mechanical sharpening as the comparison group. The results revealed that MW sharpening enhanced abrasive integrity when compared to mechanical methods, albeit with minor graphitization and localized oxidative damage occurring. Furthermore, after being sharpened by the MW method, the grit protrusion height increased, demonstrating good uniformity, and simultaneously exhibiting a higher number of effective abrasives. Noticeable craters formed in proximity to the abrasives, augmenting chip space, but sputtering led to the formation of metal deposition layers on the abrasive surfaces. The MW-sharpened wheel exhibited superior grinding wear ratios, with dynamic grinding ratios initially increasing and subsequently decreasing as the grinding process progressed. These enhancements in surface morphology allowed the MW-sharpened MD-GW to remove zirconia ceramics in a ductile manner, resulting in improved grinding surface quality. The importance of this study lies in the development of an innovative sharpening technique that improved the surface morphology quality of MD-GW, with potential ramifications for enhancing the efficiency and quality of grinding difficult-to-machine materials.
While microwave (MW) discharge technology has been developed to address the challenges inherent in sharpening metal-bonded diamond grinding wheels (MD-GW), the surface morphology and grinding performance characteristics of wheels processed through this method remain insufficiently characterized and warrant further investigation. This study employed an in-situ experimental setup to analyze MD-GW sharpened through MW discharge, with a focus on abrasive damage, grit protrusion height and uniformity, the number of effective abrasives, chip space, and bond morphology. The grinding performance of MW-sharpened MD-GW was assessed based on dynamic grinding ratios and surface quality in zirconia grinding experiments, using mechanical sharpening as the comparison group. The results revealed that MW sharpening enhanced abrasive integrity when compared to mechanical methods, albeit with minor graphitization and localized oxidative damage occurring. Furthermore, after being sharpened by the MW method, the grit protrusion height increased, demonstrating good uniformity, and simultaneously exhibiting a higher number of effective abrasives. Noticeable craters formed in proximity to the abrasives, augmenting chip space, but sputtering led to the formation of metal deposition layers on the abrasive surfaces. The MW-sharpened wheel exhibited superior grinding wear ratios, with dynamic grinding ratios initially increasing and subsequently decreasing as the grinding process progressed. These enhancements in surface morphology allowed the MW-sharpened MD-GW to remove zirconia ceramics in a ductile manner, resulting in improved grinding surface quality. The importance of this study lies in the development of an innovative sharpening technique that improved the surface morphology quality of MD-GW, with potential ramifications for enhancing the efficiency and quality of grinding difficult-to-machine materials.
2026, 39: 100028.
doi: 10.1016/j.cjme.2025.100028
Abstract:
There is a strong coupling relationship between the friction characteristics of the ball-groove interface and the ball motion behavior. However, available studies tend to consider ball motion and frictional behavior separately. In this paper, a unified tribology-kinematic model is established considering the coupling effect between friction and the ball velocity vector. The friction in ball-groove contact and ball speed are simultaneously measured by a newly developed disc-ball-disc device for studying friction and movement in ball screws. A comprehensive analysis of rubbing interface behavior and ball motion is conducted. The results show that the coupling effect between friction in ball-groove contact and ball motion is quite obvious. The sliding velocity of the ball is much higher with coupling effect than that when ignoring coupling influence, especially at high-speed conditions. The friction in ball-groove contact decreases at first and then shows a dramatic increase with the gradual rise of rotation speed, which is caused by the coupling variation of sliding speed. The studies show that the disc-ball-disc approach is an innovative and valuable method to investigate friction and ball motion in ball screws.
There is a strong coupling relationship between the friction characteristics of the ball-groove interface and the ball motion behavior. However, available studies tend to consider ball motion and frictional behavior separately. In this paper, a unified tribology-kinematic model is established considering the coupling effect between friction and the ball velocity vector. The friction in ball-groove contact and ball speed are simultaneously measured by a newly developed disc-ball-disc device for studying friction and movement in ball screws. A comprehensive analysis of rubbing interface behavior and ball motion is conducted. The results show that the coupling effect between friction in ball-groove contact and ball motion is quite obvious. The sliding velocity of the ball is much higher with coupling effect than that when ignoring coupling influence, especially at high-speed conditions. The friction in ball-groove contact decreases at first and then shows a dramatic increase with the gradual rise of rotation speed, which is caused by the coupling variation of sliding speed. The studies show that the disc-ball-disc approach is an innovative and valuable method to investigate friction and ball motion in ball screws.
2026, 39: 100026.
doi: 10.1016/j.cjme.2025.100026
Abstract:
Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety. One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system. Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure, defective mechanical seals are often replaced empirically, which not only contributes to economic losses but also poses risks to reactor safety. To reveal the mechanism by which surface defects affect sealing performance, this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals. A defect characterization model was established by extracting key features of the observed defects. The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model. Changes in sealing gap, leakage rates, and film stiffness with respect to defect size, location, and other characteristics are discussed. This work contributes to a deeper understanding of defect failure mechanisms. These results can serve as a reference for evaluating defective seals.
Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety. One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system. Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure, defective mechanical seals are often replaced empirically, which not only contributes to economic losses but also poses risks to reactor safety. To reveal the mechanism by which surface defects affect sealing performance, this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals. A defect characterization model was established by extracting key features of the observed defects. The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model. Changes in sealing gap, leakage rates, and film stiffness with respect to defect size, location, and other characteristics are discussed. This work contributes to a deeper understanding of defect failure mechanisms. These results can serve as a reference for evaluating defective seals.
2026, 39: 100048.
doi: 10.1016/j.cjme.2025.100048
Abstract:
As environmental awareness, waste minimization and conscious living gain widespread attention, endeavors have been dedicated to acquiring new water-based (WB) lubricants for green manufacturing. Zeolitic Imidazolate Framework-8 (ZIF-8), an important branch of metal organic frameworks (MOFs), can perform exceptional lubricity add-on water conditions. Nevertheless, surface functionalization of ZIF-8 nanoparticles is a key issue for promoting interfacial consistency and mixing stability in aqueous solutions. In this research work, the functionalization approach of ZIF-8 via polydopamine polymerization process (PDA) was adopted to enhance its dispersion durability in water. A ball-on-flat linear reciprocating tribometer was used to test the lubrication achievement of the functionalized ZIF-8@PDA as an additive nanoparticle in water under room temperature. The experimental findings revealed a significant lubrication improvement, with the coefficient of friction (COF) being decreased by 33.9% with a 4.0 wt% content of ZIF-8@PDA. The wear track width distance saw a reduction of 34.4% when utilizing a 2.0 wt% inclusion of ZIF-8@PDA. This lubrication enhancement accounts for microbearing and mending effects provided by ZIF-8@PDA.
As environmental awareness, waste minimization and conscious living gain widespread attention, endeavors have been dedicated to acquiring new water-based (WB) lubricants for green manufacturing. Zeolitic Imidazolate Framework-8 (ZIF-8), an important branch of metal organic frameworks (MOFs), can perform exceptional lubricity add-on water conditions. Nevertheless, surface functionalization of ZIF-8 nanoparticles is a key issue for promoting interfacial consistency and mixing stability in aqueous solutions. In this research work, the functionalization approach of ZIF-8 via polydopamine polymerization process (PDA) was adopted to enhance its dispersion durability in water. A ball-on-flat linear reciprocating tribometer was used to test the lubrication achievement of the functionalized ZIF-8@PDA as an additive nanoparticle in water under room temperature. The experimental findings revealed a significant lubrication improvement, with the coefficient of friction (COF) being decreased by 33.9% with a 4.0 wt% content of ZIF-8@PDA. The wear track width distance saw a reduction of 34.4% when utilizing a 2.0 wt% inclusion of ZIF-8@PDA. This lubrication enhancement accounts for microbearing and mending effects provided by ZIF-8@PDA.
2026, 39: 100010.
doi: 10.1016/j.cjme.2025.100010
Abstract:
Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles, reducing system reliability and posing significant challenges to driving safety. To enhance the system resilience, this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger (AET) mechanism to realize stable, reliable, and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults. First, to capture the uncertain and time-varying nature of tire cornering stiffness, an effective Takagi-Sugeno (T-S) fuzzy tire model is developed. Then, by employing the distribution-based probabilistic approach, two sets of unrelated random variables, random sensor and actuator faults in the control system, are modeled. Next, to improve communication efficiency and address ineluctable network-induced delays, an AET control framework with a well-designed triggering condition is established. Subsequently, a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞ performance is designed by using the Lyapunov-Krasovski functional method. Furthermore, the mean-square exponential stability of the closed-loop system is rigorously guaranteed. Finally, real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and effectiveness of the proposed control strategy.
Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles, reducing system reliability and posing significant challenges to driving safety. To enhance the system resilience, this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger (AET) mechanism to realize stable, reliable, and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults. First, to capture the uncertain and time-varying nature of tire cornering stiffness, an effective Takagi-Sugeno (T-S) fuzzy tire model is developed. Then, by employing the distribution-based probabilistic approach, two sets of unrelated random variables, random sensor and actuator faults in the control system, are modeled. Next, to improve communication efficiency and address ineluctable network-induced delays, an AET control framework with a well-designed triggering condition is established. Subsequently, a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞ performance is designed by using the Lyapunov-Krasovski functional method. Furthermore, the mean-square exponential stability of the closed-loop system is rigorously guaranteed. Finally, real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and effectiveness of the proposed control strategy.
2026, 39: 100063.
doi: 10.1016/j.cjme.2025.100063
Abstract:
The tire acoustic cavity resonance (TACR) noise is a significant source of the structure-borne noise inside a vehicle in the low-frequency range. This paper studies the noise dissipation effect of porous materials in reducing the TACR noise, an attempt to clarify the acoustic reduction mechanism and improve the accompanying vehicle interior noise level. A numerical model of a simplified tire cavity with rigid boundaries and acoustic excitation is established and further validated by the experiment. The effects of porous parameters on TACR frequency and sound pressure are then investigated and compared. The result reveals that the most influential material parameters are the porosity and material volume. It is also shown that the effectiveness of porous material in the mitigation of noise originates from the curliness of the material, which results in much larger acoustic impedance near the excitation position. Therefore, the sound absorption performance of the cavity attached with porous material proves to be excellent compared to that of the porous material itself. For further studying the damping effects of structural coupling, the flexible boundary of the tire tread is introduced. The results show that the porosity, material volume and structural loss factor of the tread all play important roles in reducing TACR noise.
The tire acoustic cavity resonance (TACR) noise is a significant source of the structure-borne noise inside a vehicle in the low-frequency range. This paper studies the noise dissipation effect of porous materials in reducing the TACR noise, an attempt to clarify the acoustic reduction mechanism and improve the accompanying vehicle interior noise level. A numerical model of a simplified tire cavity with rigid boundaries and acoustic excitation is established and further validated by the experiment. The effects of porous parameters on TACR frequency and sound pressure are then investigated and compared. The result reveals that the most influential material parameters are the porosity and material volume. It is also shown that the effectiveness of porous material in the mitigation of noise originates from the curliness of the material, which results in much larger acoustic impedance near the excitation position. Therefore, the sound absorption performance of the cavity attached with porous material proves to be excellent compared to that of the porous material itself. For further studying the damping effects of structural coupling, the flexible boundary of the tire tread is introduced. The results show that the porosity, material volume and structural loss factor of the tread all play important roles in reducing TACR noise.
2026, 39: 100062.
doi: 10.1016/j.cjme.2025.100062
Abstract:
When the vehicle is on a split-μ ramp and is restricted by the differential's torque-equalizing characteristic, a significant functional conflict may occur between the driving force limitation requirement of the acceleration slip regulation(ASR) control strategy for low-adhesion side wheels and the driving force enhancement demand of the entire vehicle during hill starting. Aiming at this problem, this paper proposes an ASR control strategy for the start-up of the electric vehicle on the split-μ ramp based on the combined control architecture of the service brake system and the parking brake system. First, the relationship between the driving force, braking force, and road adhesion at the slip moment is analyzed, and the ground adhesion estimation algorithm based on the dynamic equation of the driving wheel slip moment is proposed. Second, a service braking torque control model is constructed, and an ASR controller method based on the super-twisting sliding mode control (STSMC) algorithm is proposed to study the hill start assist(HSA) control strategy of electric vehicles on split-μ roads. Finally, the proposed control strategy is simulated in this paper. The simulation results show that the proposed strategy can accurately estimate the ground adhesion of the driving wheel on the low-adhesion side effectively preventing the vehicle from slipping back, and improving the dynamic performance of the vehicle under harsh conditions.
When the vehicle is on a split-μ ramp and is restricted by the differential's torque-equalizing characteristic, a significant functional conflict may occur between the driving force limitation requirement of the acceleration slip regulation(ASR) control strategy for low-adhesion side wheels and the driving force enhancement demand of the entire vehicle during hill starting. Aiming at this problem, this paper proposes an ASR control strategy for the start-up of the electric vehicle on the split-μ ramp based on the combined control architecture of the service brake system and the parking brake system. First, the relationship between the driving force, braking force, and road adhesion at the slip moment is analyzed, and the ground adhesion estimation algorithm based on the dynamic equation of the driving wheel slip moment is proposed. Second, a service braking torque control model is constructed, and an ASR controller method based on the super-twisting sliding mode control (STSMC) algorithm is proposed to study the hill start assist(HSA) control strategy of electric vehicles on split-μ roads. Finally, the proposed control strategy is simulated in this paper. The simulation results show that the proposed strategy can accurately estimate the ground adhesion of the driving wheel on the low-adhesion side effectively preventing the vehicle from slipping back, and improving the dynamic performance of the vehicle under harsh conditions.
2026, 39: 100042.
doi: 10.1016/j.cjme.2025.100042
Abstract:
Electrochemical models, characterized by high fidelity and physical interpretability, have been applied in various fields such as fast charging, battery state estimation, and battery material design. Currently, widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions. In this work, an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed. Two particles are employed to represent the electrode characteristics of the positive and negative electrodes, respectively. Through theoretical derivation, mathematical equations are established to describe various processes within the battery, including solid-phase diffusion, liquid-phase diffusion, reaction polarization, and ohmic polarization. In addition, a method for obtaining model parameters is proposed. Finally, the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions. The identified battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
Electrochemical models, characterized by high fidelity and physical interpretability, have been applied in various fields such as fast charging, battery state estimation, and battery material design. Currently, widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions. In this work, an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed. Two particles are employed to represent the electrode characteristics of the positive and negative electrodes, respectively. Through theoretical derivation, mathematical equations are established to describe various processes within the battery, including solid-phase diffusion, liquid-phase diffusion, reaction polarization, and ohmic polarization. In addition, a method for obtaining model parameters is proposed. Finally, the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions. The identified battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
2026, 39: 100009.
doi: 10.1016/j.cjme.2025.100009
Abstract:
Mode shift is a special mechanism for a power-split hybrid electric vehicle (HEV) to realise electrically variable transmission, but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock. Therefore, a smooth transient process, among other concerns, typically associated with this category of vehicles, is of great importance. The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore improve ride comfort. To this end, a dynamic model of the objective power-split HEV is first built. To resolve the contention between vehicle jerk and clutch friction loss, a model predictive control (MPC) combined with control allocation (CA) is then designed for the clutch-engaged phase. To reduce the torque fluctuation caused by the inertia torques of multiple power sources, a dynamic compensation control strategy (DCCS) that coordinates motor–generator torque to compensate for the transition torque is proposed for the brake-disengaged phase. Finally, the proposed control strategy is validated by simulation and bench test, and results show great potential in reducing shift duration, torque variation, vehicle jerk and friction loss (the simulation results show decreases of 22%, 39%, 83% and 53%, and the experimental results show decreases of 21%, 74%, 77%, and 59%, respectively), thereby improving shift quality.
Mode shift is a special mechanism for a power-split hybrid electric vehicle (HEV) to realise electrically variable transmission, but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock. Therefore, a smooth transient process, among other concerns, typically associated with this category of vehicles, is of great importance. The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore improve ride comfort. To this end, a dynamic model of the objective power-split HEV is first built. To resolve the contention between vehicle jerk and clutch friction loss, a model predictive control (MPC) combined with control allocation (CA) is then designed for the clutch-engaged phase. To reduce the torque fluctuation caused by the inertia torques of multiple power sources, a dynamic compensation control strategy (DCCS) that coordinates motor–generator torque to compensate for the transition torque is proposed for the brake-disengaged phase. Finally, the proposed control strategy is validated by simulation and bench test, and results show great potential in reducing shift duration, torque variation, vehicle jerk and friction loss (the simulation results show decreases of 22%, 39%, 83% and 53%, and the experimental results show decreases of 21%, 74%, 77%, and 59%, respectively), thereby improving shift quality.
Mechanical-thermal coupling model and fatigue life analysis of axle-box bearings of high-speed train
2026, 39: 100033.
doi: 10.1016/j.cjme.2025.100033
Abstract:
The axle box bearings of high-speed trains often operate in extremely harsh environments, bearing loads from different directions. Long-term operation and frequent changes in working conditions can easily lead to axle box bearing failures. Therefore, it is extremely important to study the mechanism of axle box bearings. Firstly, the medium of thermal deformation establishes a coupling relationship between the system dynamics model and the thermal grid model, and then obtains the thermal force coupling model of the high-speed train axle box bearing. The coupling model is validated from the perspectives of system dynamics response and temperature response, proving its effectiveness in system dynamics response and temperature characteristic response. Comparing the coupling model with the dynamics model, it is found that thermal deformation complicates the dynamic response. Finally, using the Lundberg-Palmgren (L-P) bearing fatigue calculation method and damage accumulation theory, the bearing fatigue life is calculated, and it is found that thermal deformation deteriorates the bearing operating environment, reducing the bearing fatigue life. Finally, by comparing the bearing fatigue life under different working conditions, it is concluded that the faster the vehicle speed, the greater the load, and the smaller the initial radial clearance of the bearing, the fatigue life of the bearing is reduced. The shorter the lifespan.
The axle box bearings of high-speed trains often operate in extremely harsh environments, bearing loads from different directions. Long-term operation and frequent changes in working conditions can easily lead to axle box bearing failures. Therefore, it is extremely important to study the mechanism of axle box bearings. Firstly, the medium of thermal deformation establishes a coupling relationship between the system dynamics model and the thermal grid model, and then obtains the thermal force coupling model of the high-speed train axle box bearing. The coupling model is validated from the perspectives of system dynamics response and temperature response, proving its effectiveness in system dynamics response and temperature characteristic response. Comparing the coupling model with the dynamics model, it is found that thermal deformation complicates the dynamic response. Finally, using the Lundberg-Palmgren (L-P) bearing fatigue calculation method and damage accumulation theory, the bearing fatigue life is calculated, and it is found that thermal deformation deteriorates the bearing operating environment, reducing the bearing fatigue life. Finally, by comparing the bearing fatigue life under different working conditions, it is concluded that the faster the vehicle speed, the greater the load, and the smaller the initial radial clearance of the bearing, the fatigue life of the bearing is reduced. The shorter the lifespan.
2026, 39: 100004.
doi: 10.1016/j.cjme.2025.100004
Abstract:
Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges, leading to critical failures like bolt loosening and rack fractures. This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory, integrating gear-rack meshing and wheel-rail contact mechanisms. The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading. Numerical simulations, conducted using finite element analysis, reveal peak vibration accelerations of 1.3 m/s2 for the rack, 3.0 m/s2 for the rail, 1.2 m/s2 for the sleeper, and 0.1 m/s2 for the bridge, with maximum stresses of 3 MPa in the rack, 8 MPa in the rail, and 25 MPa in connecting bolts. The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading, exceeding material strength limits at 60-meter spans. An innovative elastic connection method is proposed to mitigate stress concentrations effectively, enhancing system durability. This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems, validated through extensive simulations, and provides a practical solution for improving structural resilience, offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.
Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges, leading to critical failures like bolt loosening and rack fractures. This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory, integrating gear-rack meshing and wheel-rail contact mechanisms. The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading. Numerical simulations, conducted using finite element analysis, reveal peak vibration accelerations of 1.3 m/s2 for the rack, 3.0 m/s2 for the rail, 1.2 m/s2 for the sleeper, and 0.1 m/s2 for the bridge, with maximum stresses of 3 MPa in the rack, 8 MPa in the rail, and 25 MPa in connecting bolts. The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading, exceeding material strength limits at 60-meter spans. An innovative elastic connection method is proposed to mitigate stress concentrations effectively, enhancing system durability. This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems, validated through extensive simulations, and provides a practical solution for improving structural resilience, offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.
2026, 39: 100066.
doi: 10.1016/j.cjme.2025.100066
Abstract:
As China's high-speed railway technology advances, high-speed trains have emerged as a pivotal mode of transportation, instrumental in facilitating passenger and freight mobility while fostering robust regional economic and trade interactions. Nonetheless, the safety of train operations remains a paramount concern, prompting extensive research into the dynamic behavior of critical components, which is essential to ensuring seamless and secure transportation services. This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings. Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state, alongside delving into the research methodologies employed in developing multi-physics field coupling models. Subsequently, it expounds on the content of investigations focusing on various wheel and track impairments, grounded in the dynamic modeling of the bearing vehicle coupling system. Concurrently, the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated. Concludingly, the article underscores the inadequacy of current multi-source fault diagnosis methodologies in tackling the intricacies of complex train operating environments, thereby highlighting its significance as a pressing and vital research agenda for the future.
As China's high-speed railway technology advances, high-speed trains have emerged as a pivotal mode of transportation, instrumental in facilitating passenger and freight mobility while fostering robust regional economic and trade interactions. Nonetheless, the safety of train operations remains a paramount concern, prompting extensive research into the dynamic behavior of critical components, which is essential to ensuring seamless and secure transportation services. This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings. Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state, alongside delving into the research methodologies employed in developing multi-physics field coupling models. Subsequently, it expounds on the content of investigations focusing on various wheel and track impairments, grounded in the dynamic modeling of the bearing vehicle coupling system. Concurrently, the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated. Concludingly, the article underscores the inadequacy of current multi-source fault diagnosis methodologies in tackling the intricacies of complex train operating environments, thereby highlighting its significance as a pressing and vital research agenda for the future.
2026, 39: 100046.
doi: 10.1016/j.cjme.2025.100046
Abstract:
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles, metal-fiber reinforced polymer (FRP) hybrid thin-walled tubes (MFHTWTs) integrate the toughness, strength and lightweight of two distinct material characteristics. MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture, demonstrating significant engineering value in passive safety. This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs, aiming to demonstrate that a deeply integrated approach combining topology and parameter optimization can realize an optimal design method for MFHTWTs, thereby maximizing the functional utilization of limited material. Firstly, the review highlights the crashworthiness topology optimization methods (CTOMs) based on thin-walled structures. With a particular focus on metal, the review discusses both the practical applicability and limitation of CTOMs under crash conditions. Additionally, based on the methodology of the equivalent static load method (ESLM), the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness optimization of MFHTWTs. Furthermore, to couple structural parameter and configuration characteristics, integrated topology optimization methods, including parameter optimization, are proposed to provide a valuable reference for the global optimization of MFHTWTs. Thus, these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles, metal-fiber reinforced polymer (FRP) hybrid thin-walled tubes (MFHTWTs) integrate the toughness, strength and lightweight of two distinct material characteristics. MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture, demonstrating significant engineering value in passive safety. This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs, aiming to demonstrate that a deeply integrated approach combining topology and parameter optimization can realize an optimal design method for MFHTWTs, thereby maximizing the functional utilization of limited material. Firstly, the review highlights the crashworthiness topology optimization methods (CTOMs) based on thin-walled structures. With a particular focus on metal, the review discusses both the practical applicability and limitation of CTOMs under crash conditions. Additionally, based on the methodology of the equivalent static load method (ESLM), the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness optimization of MFHTWTs. Furthermore, to couple structural parameter and configuration characteristics, integrated topology optimization methods, including parameter optimization, are proposed to provide a valuable reference for the global optimization of MFHTWTs. Thus, these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
2026, 39: 100003.
doi: 10.1016/j.cjme.2025.100003
Abstract:
During the process of rail grinding, the local high temperature generated in the grinding contact area can affect the physical properties of the rail, thereby affecting its service performance. Therefore, studying the temperature field of rail grinding is of great significance for improving the quality of rail grinding. In this paper, a calculation model for the grinding depth and contour of the semi elliptical contact area was established based on the contact geometry relationship between the steel rail and the abrasive belt for the first time, and the influence of grinding process parameters on the parameters of the contact area was elucidated. Combined with the characteristics of steel rail abrasive belt grinding process, a calculation model for heat flux density in the semi elliptical contact area was obtained and verified. Based on the above research results, the temperature field of the moving surface heat source with continuous action in the semi elliptical contact area is solved by discretization. Research has shown that under the set grinding process parameters, the simulation and theoretical temperature changes of the rail grinding surface in the semi elliptical contact area are similar and almost reach the maximum temperature at the same time. The relative error between the simulation and theoretical maximum temperature is 6.14 %. Comparative analysis of theoretical calculations and simulation of maximum temperature under different grinding speeds shows good consistency in size and trend. The correctness of the above theoretical model has been verified through existing research results. This study proposes a new method for calculating the temperature field in the actual semi elliptical grinding area considering the rail profile, which has important theoretical significance for the calculation of the temperature field and stress field in the grinding area.
During the process of rail grinding, the local high temperature generated in the grinding contact area can affect the physical properties of the rail, thereby affecting its service performance. Therefore, studying the temperature field of rail grinding is of great significance for improving the quality of rail grinding. In this paper, a calculation model for the grinding depth and contour of the semi elliptical contact area was established based on the contact geometry relationship between the steel rail and the abrasive belt for the first time, and the influence of grinding process parameters on the parameters of the contact area was elucidated. Combined with the characteristics of steel rail abrasive belt grinding process, a calculation model for heat flux density in the semi elliptical contact area was obtained and verified. Based on the above research results, the temperature field of the moving surface heat source with continuous action in the semi elliptical contact area is solved by discretization. Research has shown that under the set grinding process parameters, the simulation and theoretical temperature changes of the rail grinding surface in the semi elliptical contact area are similar and almost reach the maximum temperature at the same time. The relative error between the simulation and theoretical maximum temperature is 6.14 %. Comparative analysis of theoretical calculations and simulation of maximum temperature under different grinding speeds shows good consistency in size and trend. The correctness of the above theoretical model has been verified through existing research results. This study proposes a new method for calculating the temperature field in the actual semi elliptical grinding area considering the rail profile, which has important theoretical significance for the calculation of the temperature field and stress field in the grinding area.
2026, 39: 100065.
doi: 10.1016/j.cjme.2025.100065
Abstract:
One-dimensional ensemble dispersion entropy (EDE1D) is an effective nonlinear dynamic analysis method for complexity measurement of time series. However, it is only restricted to assessing the complexity of one-dimensional time series (TS1D) with the extracted complexity features only at a single scale. Aiming at these problems, a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy (CMEGDE2D) is proposed in this paper. First, the TS1D is transformed into a two-dimensional image (I2D) by using Gramian angular fields (GAF) with more internal data structures and geometric features, which preserve the global characteristics and time dependence of vibration signals. Second, the I2D is analyzed at multiple scales through the composite coarse-graining method, which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods. Subsequently, a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE2D for fault feature extraction and the chicken swarm algorithm optimized support vector machine (CSO-SVM) for fault pattern identification. The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method. The results demonstrate that the proposed method has stronger discrimination ability, higher fault diagnosis accuracy and better stability than the other compared methods.
One-dimensional ensemble dispersion entropy (EDE1D) is an effective nonlinear dynamic analysis method for complexity measurement of time series. However, it is only restricted to assessing the complexity of one-dimensional time series (TS1D) with the extracted complexity features only at a single scale. Aiming at these problems, a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy (CMEGDE2D) is proposed in this paper. First, the TS1D is transformed into a two-dimensional image (I2D) by using Gramian angular fields (GAF) with more internal data structures and geometric features, which preserve the global characteristics and time dependence of vibration signals. Second, the I2D is analyzed at multiple scales through the composite coarse-graining method, which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods. Subsequently, a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE2D for fault feature extraction and the chicken swarm algorithm optimized support vector machine (CSO-SVM) for fault pattern identification. The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method. The results demonstrate that the proposed method has stronger discrimination ability, higher fault diagnosis accuracy and better stability than the other compared methods.
2026, 39: 100031.
doi: 10.1016/j.cjme.2025.100031
Abstract:
Continual learning fault diagnosis (CLFD) has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios. However, existing CLFD methods typically assume balanced task distributions, neglecting the long-tailed nature of real-world fault occurrences, where certain faults dominate while others are rare. Due to the long-tailed distribution among different mechanical conditions, excessive attention has been focused on the dominant type, leading to performance degradation in rarer types. In this paper, decoupling incremental classifier and representation learning (DICRL) is proposed to address the dual challenges of catastrophic forgetting introduced by incremental tasks and the bias in long-tailed CLFD (LT-CLFD). The core innovation lies in the structural decoupling of incremental classifier learning and representation learning. An instance-balanced sampling strategy is employed to learn more discriminative deep representations from the exemplars selected by the herding algorithm and new data. Then, the previous classifiers are frozen to prevent damage to representation learning during backward propagation. Cosine normalization classifier with learnable weight scaling is trained using a class-balanced sampling strategy to enhance classification accuracy. Experimental results demonstrate that DICRL outperforms existing continual learning methods across multiple benchmarks, demonstrating superior performance and robustness in both LT-CLFD and conventional CLFD. DICRL effectively tackles both catastrophic forgetting and long-tailed distribution in CLFD, enabling more reliable fault diagnosis in industrial applications.
Continual learning fault diagnosis (CLFD) has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios. However, existing CLFD methods typically assume balanced task distributions, neglecting the long-tailed nature of real-world fault occurrences, where certain faults dominate while others are rare. Due to the long-tailed distribution among different mechanical conditions, excessive attention has been focused on the dominant type, leading to performance degradation in rarer types. In this paper, decoupling incremental classifier and representation learning (DICRL) is proposed to address the dual challenges of catastrophic forgetting introduced by incremental tasks and the bias in long-tailed CLFD (LT-CLFD). The core innovation lies in the structural decoupling of incremental classifier learning and representation learning. An instance-balanced sampling strategy is employed to learn more discriminative deep representations from the exemplars selected by the herding algorithm and new data. Then, the previous classifiers are frozen to prevent damage to representation learning during backward propagation. Cosine normalization classifier with learnable weight scaling is trained using a class-balanced sampling strategy to enhance classification accuracy. Experimental results demonstrate that DICRL outperforms existing continual learning methods across multiple benchmarks, demonstrating superior performance and robustness in both LT-CLFD and conventional CLFD. DICRL effectively tackles both catastrophic forgetting and long-tailed distribution in CLFD, enabling more reliable fault diagnosis in industrial applications.
2026, 39: 100045.
doi: 10.1016/j.cjme.2025.100045
Abstract:
Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine. Its structural and operational characteristics make it prone to frequent and highly hazardous faults, which can easily lead to catastrophic accidents such as the failure of the entire rotor system. Therefore, it is significant to realize vibration fault monitoring and warning of inter-shaft bearings. Aero-engine vibration has complex characteristics, with diagnostic methods facing major limitations. Firstly, the compact engine structure and inter-shaft bearing placement cause weak, attenuated fault signals with significant interference, complicating feature extraction. Secondly, in counter-rotating dual-rotor systems, inter-shaft bearing components rotate oppositely, producing higher characteristic frequencies than co-rotating bearings. High-frequency fault signals often overlap or appear harmonic, and their propagation is easily affected by structural complexities, making accurate monitoring challenging. To address these challenges, a method combining Traversal Index Enhanced-gram (TIEgram) and autocorrelation (AC) is proposed for extracting weak fault features in inter-shaft bearings. TIEgram selects the optimal frequency band for resonance demodulation, isolating fault-related signal components. To counter non-stationary signals from aero-engine dynamics, slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals. Autocorrelation analysis then yields the squared envelope autocorrelation spectrum, compared with bearing fault characteristic orders for diagnosis. Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.
Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine. Its structural and operational characteristics make it prone to frequent and highly hazardous faults, which can easily lead to catastrophic accidents such as the failure of the entire rotor system. Therefore, it is significant to realize vibration fault monitoring and warning of inter-shaft bearings. Aero-engine vibration has complex characteristics, with diagnostic methods facing major limitations. Firstly, the compact engine structure and inter-shaft bearing placement cause weak, attenuated fault signals with significant interference, complicating feature extraction. Secondly, in counter-rotating dual-rotor systems, inter-shaft bearing components rotate oppositely, producing higher characteristic frequencies than co-rotating bearings. High-frequency fault signals often overlap or appear harmonic, and their propagation is easily affected by structural complexities, making accurate monitoring challenging. To address these challenges, a method combining Traversal Index Enhanced-gram (TIEgram) and autocorrelation (AC) is proposed for extracting weak fault features in inter-shaft bearings. TIEgram selects the optimal frequency band for resonance demodulation, isolating fault-related signal components. To counter non-stationary signals from aero-engine dynamics, slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals. Autocorrelation analysis then yields the squared envelope autocorrelation spectrum, compared with bearing fault characteristic orders for diagnosis. Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.
2026, 39: 100089.
doi: 10.1016/j.cjme.2025.100089
Abstract:
The automatic loading systems of artillery are critical for the accurate, efficient, and reliable delivery of projectiles and propellants into the gun chamber. In modern artillery, the ammunition conveyor serves as the end effector of the automatic loading system, and its motion state significantly impacts the accuracy of projectiles. Therefore, it is of immense importance to precisely and effectively evaluate the reliability of the motion accuracy of the ammunition conveyor. This paper aims to propose a practical and efficient analysis method for evaluating the reliability of the motion accuracy of the ammunition conveyor. The proposed approach involves the use of a deep learning network to approximate the physical model and the extremum method to obtain a single cycle sequence decoupling strategy for solving the time-varying reliability issue of complex systems. Employing this strategy, the time-varying reliability of the ammunition conveyor is transformed into a static reliability problem. The proposed method includes the use of a deep feedforward neural network, second-order saddle point approximation (SPA) method, extremum method, and efficient global optimization (EGO) technology. The results reveal that the reliability of the motion accuracy of the ammunition conveyor is 93.42%, with the maximum failure probability occurring at 0.21 s. These results serve as an important reference for the structural optimization design of the ammunition conveyor based on reliability and the maintenance of the operational process.
The automatic loading systems of artillery are critical for the accurate, efficient, and reliable delivery of projectiles and propellants into the gun chamber. In modern artillery, the ammunition conveyor serves as the end effector of the automatic loading system, and its motion state significantly impacts the accuracy of projectiles. Therefore, it is of immense importance to precisely and effectively evaluate the reliability of the motion accuracy of the ammunition conveyor. This paper aims to propose a practical and efficient analysis method for evaluating the reliability of the motion accuracy of the ammunition conveyor. The proposed approach involves the use of a deep learning network to approximate the physical model and the extremum method to obtain a single cycle sequence decoupling strategy for solving the time-varying reliability issue of complex systems. Employing this strategy, the time-varying reliability of the ammunition conveyor is transformed into a static reliability problem. The proposed method includes the use of a deep feedforward neural network, second-order saddle point approximation (SPA) method, extremum method, and efficient global optimization (EGO) technology. The results reveal that the reliability of the motion accuracy of the ammunition conveyor is 93.42%, with the maximum failure probability occurring at 0.21 s. These results serve as an important reference for the structural optimization design of the ammunition conveyor based on reliability and the maintenance of the operational process.
2026, 39: 100016.
doi: 10.1016/j.cjme.2025.100016
Abstract:
The aerostatic spindle is a key component of ultra-precision machine tools, and its error motion is crucial to machining accuracy and reliability. Spindle error motion is unavoidable, and its online monitoring and prediction are quite important. Currently, there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle, and the level of intelligence is relatively low. To address this problem, an error motion monitoring system based on digital twin (DT) technology was established for the aerostatic spindle. A spindle error motion prediction method based on a mechanism and data fusion model (MDFM) was proposed. Additionally, a highly available and interactive aerostatic spindle DT service platform was developed. Experimental results have verified the good performance of this platform. The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle, enabling three-dimensional visualization, monitoring, prediction, and simulation of spindle error motion, and shows good potential for engineering applications.
The aerostatic spindle is a key component of ultra-precision machine tools, and its error motion is crucial to machining accuracy and reliability. Spindle error motion is unavoidable, and its online monitoring and prediction are quite important. Currently, there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle, and the level of intelligence is relatively low. To address this problem, an error motion monitoring system based on digital twin (DT) technology was established for the aerostatic spindle. A spindle error motion prediction method based on a mechanism and data fusion model (MDFM) was proposed. Additionally, a highly available and interactive aerostatic spindle DT service platform was developed. Experimental results have verified the good performance of this platform. The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle, enabling three-dimensional visualization, monitoring, prediction, and simulation of spindle error motion, and shows good potential for engineering applications.
2026, 39: 100014.
doi: 10.1016/j.cjme.2025.100014
Abstract:
Fault features in mechanical systems often manifest as transient impulses, which can be effectively analyzed using time-frequency analysis (TFA) methods. Recently, a new TFA technique known as the time-reassigned multisynchrosqueezing transform (TMSST) was proposed to capture these transient impulses for fault diagnosis. However, the TMSST, which is based on the short-time Fourier transform (STFT), suffers from unclear high-frequency representations owing to the fixed sliding window used in the STFT. To address this limitation, the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis. Furthermore, an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition. To validate the proposed technique, a simulated noise-added signal and four experimental bearing defect datasets were used. The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions. This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors.
Fault features in mechanical systems often manifest as transient impulses, which can be effectively analyzed using time-frequency analysis (TFA) methods. Recently, a new TFA technique known as the time-reassigned multisynchrosqueezing transform (TMSST) was proposed to capture these transient impulses for fault diagnosis. However, the TMSST, which is based on the short-time Fourier transform (STFT), suffers from unclear high-frequency representations owing to the fixed sliding window used in the STFT. To address this limitation, the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis. Furthermore, an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition. To validate the proposed technique, a simulated noise-added signal and four experimental bearing defect datasets were used. The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions. This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors.
2026, 39: 100005.
doi: 10.1016/j.cjme.2025.100005
Abstract:
Current improved Empirical Mode Decomposition (EMD) methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques, thereby improving the mode decomposition performance, which is of great significance in extracting fault features from mechanical signals. However, noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions (IMFs) that obscure signal features. To address these issues, a Precise Identification-based Mode Decomposition (PIMD) method is proposed. This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identification approach, which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering, thus benefiting the extraction of more mechanical fault features. Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-to-noise ratios and perform a highly rigorous high-low frequency decomposition, significantly outperforming EMD. Finally, mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that, compared to four mainstream methods, the PIMD method exhibits the best mode decomposition performance and can extract more and clearer mechanical fault features.
Current improved Empirical Mode Decomposition (EMD) methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques, thereby improving the mode decomposition performance, which is of great significance in extracting fault features from mechanical signals. However, noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions (IMFs) that obscure signal features. To address these issues, a Precise Identification-based Mode Decomposition (PIMD) method is proposed. This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identification approach, which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering, thus benefiting the extraction of more mechanical fault features. Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-to-noise ratios and perform a highly rigorous high-low frequency decomposition, significantly outperforming EMD. Finally, mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that, compared to four mainstream methods, the PIMD method exhibits the best mode decomposition performance and can extract more and clearer mechanical fault features.
2026, 39: 100037.
doi: 10.1016/j.cjme.2025.100037
Abstract:
The application of oil debris monitoring technology to lubricating oil has gained substantial prominence as a diagnostic tool for identifying machinery and equipment wear-related issues. Among the various methods available for wear fault monitoring, the detection of changing electromagnetic fields using triple-coil inductive sensors are widely used because of its inherent simplicity of design and operational convenience in facilitating full-flow detection. However, the accuracy of this method is limited by several factors. In this study, an intricate simulation model of the internal magnetic field in a triple-coil inductive sensor was developed. Subsequently, the effects of the excitation signal frequency and wear particle composition on the magnetic flux density were analyzed. The simulation results show an optimal excitation frequency range of approximately 2−100 kHz for ferromagnetic particle detection, whereas nonferromagnetic metal particles require higher excitation frequencies. With an increase in the distance between adjacent wear particles, the magnetic coupling effect decreased rapidly. Moreover, the magnetic flux density changed from its maximum value to a minimum value as the rotation angle of the particles increased from 0° to 90°. A special experimental platform was constructed to verify the simulation results, and the experimental results were consistent with the simulation results.
The application of oil debris monitoring technology to lubricating oil has gained substantial prominence as a diagnostic tool for identifying machinery and equipment wear-related issues. Among the various methods available for wear fault monitoring, the detection of changing electromagnetic fields using triple-coil inductive sensors are widely used because of its inherent simplicity of design and operational convenience in facilitating full-flow detection. However, the accuracy of this method is limited by several factors. In this study, an intricate simulation model of the internal magnetic field in a triple-coil inductive sensor was developed. Subsequently, the effects of the excitation signal frequency and wear particle composition on the magnetic flux density were analyzed. The simulation results show an optimal excitation frequency range of approximately 2−100 kHz for ferromagnetic particle detection, whereas nonferromagnetic metal particles require higher excitation frequencies. With an increase in the distance between adjacent wear particles, the magnetic coupling effect decreased rapidly. Moreover, the magnetic flux density changed from its maximum value to a minimum value as the rotation angle of the particles increased from 0° to 90°. A special experimental platform was constructed to verify the simulation results, and the experimental results were consistent with the simulation results.
2026, 39: 100021.
doi: 10.1016/j.cjme.2025.100021
Abstract:
Excessive unbalanced vibrations of rotor-bearing systems significantly affect the stability and safety of high-end rotating machinery, such as aero engines, turbo-generators, and high-end machine tools. To realize the on-line self-recovery of unbalanced vibration faults in a rotor system, a self-recovery regulation method based on the grey wolf optimization-adaptive linear quadratic regulator (GWO-ALQR) is proposed. First, a self-recovery regulation system for unbalanced vibrations was constructed, with the state-space equation of the control system obtained and discretized based on the dynamic equation of the rotor-bearing system. Subsequently, a self-recovery regulation method for unbalanced vibrations based on GWO-ALQR was designed based on the state-space equation. In this method, the parameters of the control system are optimized using grey wolf optimization (GWO), with the working conditions identified on-line. The optimization parameters were selected independently, while the control commands were generated through a linear quadratic regulator (LQR) to control the action of the actuator to achieve self-recovery of the unbalanced vibration. The experimental results indicate that the unbalanced vibration of the rotor system can be restrained below the expected vibration threshold by the self-recovery regulation system based on GWO-ALQR and the final vibration suppression effect can exceed 70 %.
Excessive unbalanced vibrations of rotor-bearing systems significantly affect the stability and safety of high-end rotating machinery, such as aero engines, turbo-generators, and high-end machine tools. To realize the on-line self-recovery of unbalanced vibration faults in a rotor system, a self-recovery regulation method based on the grey wolf optimization-adaptive linear quadratic regulator (GWO-ALQR) is proposed. First, a self-recovery regulation system for unbalanced vibrations was constructed, with the state-space equation of the control system obtained and discretized based on the dynamic equation of the rotor-bearing system. Subsequently, a self-recovery regulation method for unbalanced vibrations based on GWO-ALQR was designed based on the state-space equation. In this method, the parameters of the control system are optimized using grey wolf optimization (GWO), with the working conditions identified on-line. The optimization parameters were selected independently, while the control commands were generated through a linear quadratic regulator (LQR) to control the action of the actuator to achieve self-recovery of the unbalanced vibration. The experimental results indicate that the unbalanced vibration of the rotor system can be restrained below the expected vibration threshold by the self-recovery regulation system based on GWO-ALQR and the final vibration suppression effect can exceed 70 %.
2026, 39: 100027.
doi: 10.1016/j.cjme.2025.100027
Abstract:
In aerospace, nuclear power, and new energy vehicles industries, utilizing integrated metal components with extreme sizes and/or structures is crucial for achieving significant weight-saving, performance-improvement, and excellent reliability. These components, made from metal sheets, rings, or tubes, exhibit characteristics like ultra-thin, ultra-thick, ultra-large, ultra-long, ultra-high ribs, and large variable diameters. During plastic deformation in metal forming processes, defects such as ruptures, wrinkles, excessive strain differences, and unexpected weak performance areas are likely to occur due to the intersection of multiple effects in different research disciplines, including materials science, processes, and mechanics of materials. Consequently, the smooth forming of integrated parts is difficult. It is the first time to review, summarize, and analyze the advancement of forming methods for producing integrated parts with extreme sizes and structures. The general academic ideas to change the process conditions and sequences to optimize stress state and improve plastic deformation ability for forming the components with extreme sizes/structures are introduced. Practical examples, discussed in detail in the paper, include the forming of (ⅰ) integrated ultra-thin and ultra-thick sheet components; (ⅱ) integrated ultra-large size ring components with thin wall and high ribs; and (ⅲ) integrated ultra-long tube components with large perimeter difference. Various plasticity technologies and process sequences have been developed. The key processes and applications of the technologies are discussed in detail, which achieve successful plastic forming of integrated components. This paper provides state-of-the-art and perspectives for the rapidly advancing material forming fields of key metal components for the next generation of equipment.
In aerospace, nuclear power, and new energy vehicles industries, utilizing integrated metal components with extreme sizes and/or structures is crucial for achieving significant weight-saving, performance-improvement, and excellent reliability. These components, made from metal sheets, rings, or tubes, exhibit characteristics like ultra-thin, ultra-thick, ultra-large, ultra-long, ultra-high ribs, and large variable diameters. During plastic deformation in metal forming processes, defects such as ruptures, wrinkles, excessive strain differences, and unexpected weak performance areas are likely to occur due to the intersection of multiple effects in different research disciplines, including materials science, processes, and mechanics of materials. Consequently, the smooth forming of integrated parts is difficult. It is the first time to review, summarize, and analyze the advancement of forming methods for producing integrated parts with extreme sizes and structures. The general academic ideas to change the process conditions and sequences to optimize stress state and improve plastic deformation ability for forming the components with extreme sizes/structures are introduced. Practical examples, discussed in detail in the paper, include the forming of (ⅰ) integrated ultra-thin and ultra-thick sheet components; (ⅱ) integrated ultra-large size ring components with thin wall and high ribs; and (ⅲ) integrated ultra-long tube components with large perimeter difference. Various plasticity technologies and process sequences have been developed. The key processes and applications of the technologies are discussed in detail, which achieve successful plastic forming of integrated components. This paper provides state-of-the-art and perspectives for the rapidly advancing material forming fields of key metal components for the next generation of equipment.
2026, 39: 100023.
doi: 10.1016/j.cjme.2025.100023
Abstract:
Common strong noise interferences like metal splashes, smoke, and arc light during welding can seriously pollute the laser stripe images, causing the tracking model to drift and leading to tracking failure. At present, there are already many mature methods for identifying and extracting feature points of linear laser stripes. When the laser stripe forms a curved shape on the surface of the workpiece, these linear methods will no longer be applicable. To eliminate interference sources enhance the robustness of the weld tracking model, and effectively extract the feature points of curved laser stripes under strong noise conditions. This paper proposes a Conditional Generative Adversarial Network(CGAN)–based anti-interference recognition method for welding images. The generator adopts an improved U-Net+ + structure, adds a Multi-scale Channel Attention module (MS-CAM), introduces Deep Supervision, and proposes a Multi-output Fusion strategy (MOFS) in the output result to enhance the image inpainting effect; the discriminator uses PatchGAN. The center of the laser stripe is obtained using the grayscale center of mass method and then combined with polynomial fitting to extract the feature points of the weld seam. The experimental results show that the PSNR of the inpainting image is 26.24 dB, the SSIM is 0.98, and the LPIPS is 0.032. The centerline of the inpainting image and the centerline of the noise-free image laser stripe are fitted with a curve. The error of centerline feature points is no more than 5%, confirming the superiority and feasibility of the method.
Common strong noise interferences like metal splashes, smoke, and arc light during welding can seriously pollute the laser stripe images, causing the tracking model to drift and leading to tracking failure. At present, there are already many mature methods for identifying and extracting feature points of linear laser stripes. When the laser stripe forms a curved shape on the surface of the workpiece, these linear methods will no longer be applicable. To eliminate interference sources enhance the robustness of the weld tracking model, and effectively extract the feature points of curved laser stripes under strong noise conditions. This paper proposes a Conditional Generative Adversarial Network(CGAN)–based anti-interference recognition method for welding images. The generator adopts an improved U-Net+ + structure, adds a Multi-scale Channel Attention module (MS-CAM), introduces Deep Supervision, and proposes a Multi-output Fusion strategy (MOFS) in the output result to enhance the image inpainting effect; the discriminator uses PatchGAN. The center of the laser stripe is obtained using the grayscale center of mass method and then combined with polynomial fitting to extract the feature points of the weld seam. The experimental results show that the PSNR of the inpainting image is 26.24 dB, the SSIM is 0.98, and the LPIPS is 0.032. The centerline of the inpainting image and the centerline of the noise-free image laser stripe are fitted with a curve. The error of centerline feature points is no more than 5%, confirming the superiority and feasibility of the method.
2026, 39: 100083.
doi: 10.1016/j.cjme.2025.100083
Abstract:
Welding deformation adversely affects the quality and precision of structural components, and traditional methods require significant material resources and time. Machine learning has demonstrated exceptional accuracy and efficiency in solving complex problems. Thus, the use of machine learning to predict welding deformations is a novel approach. In this study, laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset. The deep neural network (DNN) and convolutional neural network (CNN) models were designed and constructed, with average prediction errors of 0.85 mm and 0.94 mm on the validation set, respectively. To further optimize the network parameters, a differential evolution algorithm was employed through mutation, crossover, and selection. The results indicated that after optimization, the prediction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm, respectively. These represent accuracy improvements of 14.8% and 9.6%, respectively. The optimized models exhibited superior predictive performances for the validation set.
Welding deformation adversely affects the quality and precision of structural components, and traditional methods require significant material resources and time. Machine learning has demonstrated exceptional accuracy and efficiency in solving complex problems. Thus, the use of machine learning to predict welding deformations is a novel approach. In this study, laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset. The deep neural network (DNN) and convolutional neural network (CNN) models were designed and constructed, with average prediction errors of 0.85 mm and 0.94 mm on the validation set, respectively. To further optimize the network parameters, a differential evolution algorithm was employed through mutation, crossover, and selection. The results indicated that after optimization, the prediction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm, respectively. These represent accuracy improvements of 14.8% and 9.6%, respectively. The optimized models exhibited superior predictive performances for the validation set.
2026, 39: 100024.
doi: 10.1016/j.cjme.2025.100024
Abstract:
Biomedical applications necessitate natural or synthetic biomaterials that can maintain, improve, or even replace damaged tissue or a biological function, facilitating healing for people who have suffered from an injury or disease. Metallic biomaterials show superior mechanical properties with greater service life than other materials. Biodegradable materials can avoid the inevitable second operation of removing the implant in the case of temporary implantation, reducing the risk of infections, medical complications, healing time, and cost. Magnesium (Mg), zinc (Zn), iron (Fe), and their alloys are potential biodegradable metallic materials. The characteristics of biodegradable metallic materials are variable and depend on many factors, such as alloying elements, microstructure, existing phases, and thermomechanical treatment. The current review emphasizes the impact of alloying element addition on the characteristics of metallic biodegradable materials, with particular attention to the relationships between alloying elements, microstructure, mechanical performance, corrosion, and biocompatibility. Mg alloys show good mechanical and corrosion properties with excellent biocompatibility. Using biocompatible alloying elements can improve Mg alloy mechanical and corrosion properties without affecting their biocompatibility. However, critical limitations are still maintained, like rapid degradation and gas bubble formation. Zn alloys could overcome the limitations of Mg alloys with appropriate degradation rates, ease of casting and processing, and good biocompatibility. Alloying, particularly with Mg, Li, and Cu, combined with thermomechanical treatment, can significantly affect the microstructure and mechanical performance of Zn alloys and overcome the problem of unsuitable mechanical properties. Fe alloys have excellent mechanical performance, formability, and biocompatibility with a low degradation rate. Applying surface treatment, using novel structures, alloying with the appropriate amount of alloying elements, and using advanced manufacturing techniques may present a way to solve the problems associated with biodegradable metallic materials, which could open new horizons and increase their applicability in biomedical applications.
Biomedical applications necessitate natural or synthetic biomaterials that can maintain, improve, or even replace damaged tissue or a biological function, facilitating healing for people who have suffered from an injury or disease. Metallic biomaterials show superior mechanical properties with greater service life than other materials. Biodegradable materials can avoid the inevitable second operation of removing the implant in the case of temporary implantation, reducing the risk of infections, medical complications, healing time, and cost. Magnesium (Mg), zinc (Zn), iron (Fe), and their alloys are potential biodegradable metallic materials. The characteristics of biodegradable metallic materials are variable and depend on many factors, such as alloying elements, microstructure, existing phases, and thermomechanical treatment. The current review emphasizes the impact of alloying element addition on the characteristics of metallic biodegradable materials, with particular attention to the relationships between alloying elements, microstructure, mechanical performance, corrosion, and biocompatibility. Mg alloys show good mechanical and corrosion properties with excellent biocompatibility. Using biocompatible alloying elements can improve Mg alloy mechanical and corrosion properties without affecting their biocompatibility. However, critical limitations are still maintained, like rapid degradation and gas bubble formation. Zn alloys could overcome the limitations of Mg alloys with appropriate degradation rates, ease of casting and processing, and good biocompatibility. Alloying, particularly with Mg, Li, and Cu, combined with thermomechanical treatment, can significantly affect the microstructure and mechanical performance of Zn alloys and overcome the problem of unsuitable mechanical properties. Fe alloys have excellent mechanical performance, formability, and biocompatibility with a low degradation rate. Applying surface treatment, using novel structures, alloying with the appropriate amount of alloying elements, and using advanced manufacturing techniques may present a way to solve the problems associated with biodegradable metallic materials, which could open new horizons and increase their applicability in biomedical applications.
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