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Identification and Evaluation of Key Error Elements in Complex Composite Aviation Componts Assembly Driven by Mechanism and Data Model Fusion GUO Feiyan1, ZHANG Hui2, SONG Changjie1, ZHANG Shuo1 China Mechanical Engineering    2025, 36 (07): 1530-1543.   DOI: 10.3969/j.issn.1004-132X.2025.07.016 Abstract （1753）      PDF（pc） （6423KB）（329）       Knowledge map    Save  In composite assembly of complex aviation products, the factors such as part deformations under loads, numerous parameters and so on were considered. Deformation error source models for key assembly links caused by positioning and clamping, joining and rebounding were analyzed, and the Jacobian sensor matrix representing error transmission relationship was modified to establish assembly error transmission mechanism model. A support vector regression model was established based on assembly error data, a fusion model of mechanism model and data model was gained. With the predication and compensation model for the calculated values of the error mechanism model and the actual deviation, a Sobol sensitivity analysis method was adopted to calculate the global sensitivity coefficients of different assembly error links, and the key error elements affecting assembly accuracy was identified. Finally, the assembly of wing box component was taken as an example to prove the effectiveness of the proposed method. Reference | Related Articles | Metrics | Comments（0）

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Research on Differential Steering Mechanism Based on Tire Cornering Biaofei SHI, Xiaoming YE, Haoyu LYU, Feng LAI China Mechanical Engineering    2025, 36 (10): 2224-2231.   DOI: 10.3969/j.issn.1004-132X.2025.10.008 Abstract （1617）   HTML （14）    PDF（pc） （1816KB）（91）       Knowledge map    Save Differential steering based on tire cornering suited low-speed， large steering radius scenarios of distributed drive electric vehicles（DDEV） without steering mechanisms. In order to study the mechanism of differential steering based on tire cornering， a 7-degree-of-freedom DDEV dynamic model with no steering mechanism and PAC2002 tire model were established. Then， the formation mechanism of differential steering was analyzed and a systematic analysis method from the input of differential longitudinal force to the output of vehicle steering radius of differential steering was proposed by considering the tire force longitudinal-lateral-coupling characteristics. Leveraging the proposed systematic analysis method， the stability of differential steering and the influences of differential longitudinal force， vehicle parameters and tire characteristics on steering radius were studied. Finally， a Carsim/Simulink joint simulation platform was established to simulate differential steering under different influencing factors. The results show that within the range of tire cornering， the larger the differential longitudinal force， the larger the ratio of track width to wheelbase， and the smaller the tire lateral stiffness， the smaller the steering radius. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Flexible Job Shop Scheduling Problems Considering Limited AGV Transportation Resources Guohui ZHANG, Yihao CAI, Zhixiao LI, Shenghui GUO, Haijun ZHANG China Mechanical Engineering    2025, 36 (08): 1811-1823.   DOI: 10.3969/j.issn.1004-132X.2025.08.016 Abstract （1521）   HTML （2）    PDF（pc） （3435KB）（119）       Knowledge map    Save Aiming at the flexible job shop scheduling problems of limited AGV transportation resources in the intelligent manufacturing environments， an integrated scheduling model for limited AGV transportation resources was established with the objective of minimizing the maximum completion time， total energy consumption and the delivery penalty value of workpieces. An improved NSGA -II solution algorithm was proposed to construct a three-stage coding scheme for the integrated scheduling model， and three initialization rules were designed to improve the quality and diversity of the initial population. Combined with the critical path， an improved variable neighborhood search was proposed to enhance the local search capability of the algorithm. In the experimental part， the algorithm was compared with other algorithms using various evaluation indexes， and the experimental results show that the algorithm may effectively solve the integrated scheduling problems of limited AGV transportation resources under different sizes of standard test cases and actual production cases of aviation enterprises. Meanwhile， the effectiveness of the integrated scheduling model was analyzed under different numbers of AGVs， and it is concluded that the number of AGVs in the flexible operation workshop conforms to the law of diminishing marginal effectiveness， so as to provide a reference for the configuration of AGVs in the actual manufacturing workshop. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Mechanism Analysis and Online Monitoring System of Camshaft High-speed Grinding Burns Zhaohui DENG, Rongjin ZHUO, Jingqiang CHEN, Jimin GE, Lishu LYU, Wei LIU China Mechanical Engineering    2025, 36 (08): 1784-1795.   DOI: 10.3969/j.issn.1004-132X.2025.08.014 Abstract （1509）   HTML （2）    PDF（pc） （5261KB）（81）       Knowledge map    Save The high-speed grinding of the non-circular contours of the camshafts was prone to grinding burns， resulting in a decrease in surface quality and service life and even scrapping. Therefore， the mechanism analysis and online monitoring system of camshaft high-speed grinding burns were studied. The influences of processing parameters on grinding burns were discussed. A grinding burn's quantitative evaluation method was proposed by surface morphology and hardness.Frequency and time-frequency domain analysis methods carried out the signal processing and feature extraction. The relationship between the sensing signals and grinding burns was analyzed. The AE （acoustic emission） signal features with a high correlation with grinding burn were extracted based on ReliefF. The monitoring model of grinding burn was established based on GA-SVM（genetic algorithm-support vector machine）. And it was verified by experiments. The online monitoring system of camshaft high-speed grinding burns was developed and applied. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Thermal Image Input-based ResNet Method for Thermal Error Modeling of Machine Tool Spindles Mingfan LI, Long YANG, Sheng LI, Huan GUO, Guoqiang FU China Mechanical Engineering    2025, 36 (09): 2057-2067.   DOI: 10.3969/j.issn.1004-132X.2025.09.018 Abstract （1507）   HTML （1）    PDF（pc） （5627KB）（127）       Knowledge map    Save To achieve a high-precision and highly generalizable thermal error model of machine tools， a thermal image input-based ResNet method was proposed for thermal error modeling of CNC machine tool spindles. A thermal image dataset labelled was constructed with thermal error rounding， and a ResNet-based classification model was trained for thermal error prediction using thermal images as inputs. Considering the regression characteristics of the machine tool thermal error time series， a regression output layer was constructed by integrating the probabilities of different classification labels from the classification output layer in a weighted manner， enabling thermal error regression prediction without retraining. The deep features of thermal images and the classification performance of the ResNet model were visualized， confirming the effectiveness of ResNet in feature extraction and strong classification ability. Finally， the ResNet model was compared with GoogLeNet and VGGNet models under different operating conditions， demonstrating the high accuracy and generalization of the ResNet-based thermal error classification and regression models. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Integrated Design Technology for New Energy Vehicle Power Battery Systems SHI Peicheng1, SHAN Zixian1, ZHU Hailong1, HAI Bin2, WANG Lei2, LU Fayan2 China Mechanical Engineering    2025, 36 (07): 1611-1623.   DOI: 10.3969/j.issn.1004-132X.2025.07.024 Abstract （1494）      PDF（pc） （6886KB）（126）       Knowledge map    Save An integrated design technology of power battery systems for new energy vehicles was elaborated, and the advantages in space utilisation, range, and cost control were shown by analysing the technology such as moduleless, battery chassis integration and battery body integration. Other typical battery technology which promoted the development of automotive industry through structural innovation, thermal management optimisation and fast charging solutions were explored. The development directions of power battery system integration technology were outlooked in terms of intelligent integration, sustainable materials, and standardisation. Reference | Related Articles | Metrics | Comments（0）

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Thin-walled Workpiece Milling Deformation Error Prediction Based on Multi-source Information Fusion and Ensemble Learning YIN Jia1, ZHENG Jian2, LIU Yao3, JIA Baoguo1, DUAN Xiaorui1 China Mechanical Engineering    2025, 36 (06): 1261-1268.   DOI: 10.3969/j.issn.1004-132X.2025.06.013 Abstract （1492）      PDF（pc） （5304KB）（60）       Knowledge map    Save In practical machining processes, the dimensional accuracy of thin-walled workpiece was significantly affected by multiple factors including cutting forces, forced vibrations, chatter phenomena, geometric characteristics of workpiece and material properties, rendering deformation prediction and control particularly challenging. A multi-source information fusion method for deformation error prediction in thin-walled workpiece milling processes was developed. Machining parameters, vibration signals, and other relevant data were integrated to establish a deformation error prediction model through Stacking ensemble learning methodology, with comprehensive experimental validation performed. Comparative analyses reveal that the constructed model demonstrates superior robustness, higher accuracy, and enhanced practicality when compared with conventional data-driven prediction methods. Reference | Related Articles | Metrics | Comments（0）

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Effects of Glass Fiber Mass Fraction on W-PACIM Pipes of Long Glass Fiber Reinforced Polypropylene LIAO Qiansheng1, 3, LIU Hesheng2, KUANG Tangqing2, LIU Jiahao2, ZHANG Wei2 China Mechanical Engineering    2025, 36 (06): 1329-1337.   DOI: 10.3969/j.issn.1004-132X.2025.06.020 Abstract （1491）      PDF（pc） （6799KB）（60）       Knowledge map    Save To study the effects of glass fiber mass fraction on W-PACIM pipes with long glass fiber reinforced polypropylene as the outer material and pure polypropylene as the inner material, the influences of glass fiber mass fraction on the residual wall thickness, the orientation distribution of glass fibers and the pressure resistance of the pipes were analyzed by experimental methods. The results show that with the increases of glass fiber mass fraction, the total residual wall thickness of the pipes decreases first and then increases. The outer layer of the pipes may be divided into near the mold wall layer, the middle layer and the near interface layer according to the distribution characteristics of the glass fiber orientations, the orientation of the glass fiber along the melt flow direction increases gradually from the outside to the inside, the uniformity of the distribution of the outer glass fiber decreases with the increase of glass fiber mass fraction. The pressure resistance of pipes increases first and then decreases, when the glass fiber mass fraction is 20%, the pressure resistance of pipes is the best. Reference | Related Articles | Metrics | Comments（0）

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Ultra-precision Turning Alignment Error Compensation Technology in Multi-axis Simultaneous Operations YUAN Jiabin, GUO Xipeng, LI Rong, YIN Shaohui China Mechanical Engineering    2025, 36 (07): 1397-1406.   DOI: 10.3969/j.issn.1004-132X.2025.07.001 Abstract （1422）      PDF（pc） （9879KB）（99）       Knowledge map    Save To address the issues of ineffective compensation for aspheric machining figure errors, an XZB three-axis ultra-precision machining method was proposed. The impact of errors in tool alignment, tool radius and the deviation of tool tip relative to the B-axis rotation center on workpiece surface accuracy was analyzed, and the corresponding compensation methods were presented. Turning experiments of nickel-plated convex aspherical workpieces were conducted. Figure errors of the workpieces were reduceed from 0.6774 μm to 0.0749 μm, with the XZB three-axis linked turning method. Experimental results show that the XZB three-axis linked turning process significantly improves the surface shape accuracy and surface quality of small-diameter aspherical surfaces, compared with the XZ two-axis linked turning method. Reference | Related Articles | Metrics | Comments（0）

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Intelligent Vehicle Trajectory Planning Based on Spatio-temporal Risk Fields Huifang KONG, Chenshun WANG, Qian ZHANG, Tiankuo LIU China Mechanical Engineering    2025, 36 (10): 2463-2471.   DOI: 10.3969/j.issn.1004-132X.2025.10.036 Abstract （1295）   HTML （0）    PDF（pc） （2976KB）（63）       Knowledge map    Save Aming to describe and avoid different dimensions of risks faced by intelligent vehicles， a two-layer trajectory planning method was proposed based on spatio-temporal risk fields. Traffic elements were divided into abstract elements and concrete elements， the spatial-temporal risk fields of abstract elements based on Gaussian distribution function and concrete elements based on spatial vector were established respectively to represent the environmental risks faced by intelligent vehicles in three dimensions： vertical， horizontal and temporal. Additionally， the trajectory planning problem of intelligent vehicles was divided into path and speed dual planning problem. The longitudinal-lateral dimension risk and longitudinal-temporal dimension risk were accordingly applied to dynamic planning cost function. Then， the path and speed with the comprehensive lowest cost were calculated， and combined with quadratic programming algorithm， the path and velocity were further optimized to obtain the final trajectory. Simulation results demonstrate that the proposed methodology may effectively characterize spatio-temporal driving risks across diverse scenarios while generating constraint-satisfying trajectories， thereby significantly enhance road driving safety. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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C-warping Mechanism and Treatment Strategies in Leveling Processes of Strip Steels YANG Yonghui1, ZHANG Ji1, TAN Hailong1, NIU Baicao1, BAI Zhenhua1, 2, 3 China Mechanical Engineering    2025, 36 (06): 1345-1351.   DOI: 10.3969/j.issn.1004-132X.2025.06.022 Abstract （1286）      PDF（pc） （1401KB）（66）       Knowledge map    Save  In order to solve the problems of C-warping defects in strip steels in the leveling processes, the processing parameters in the strip leveling processes were optimized, and a set of C-warping prediction and treatment strategies for strip steels were developed. The formation mechanism of C-warping was analyzed from three aspects: rolling, processing lubrication systems and the influences of anti-wrinkle rollers and anti-trembling rollers on the forces of strip steels. The stress distribution of the horizontal direction of the outlet strips in the thickness was obtained, based on the stress analysis of the strips under the assumption of half-plane infinite body， the maximum and minimum transverse elongation in the thickness direction of the strips were obtained, and the height of the C warp of the strips was obtained through the geometric relationship. A set of optimal height settings of anti-wrinkle rollers and anti-trembling rollers were sought to minimize the objective function of the comprehensive control, and the elongation in the direction of strip thickness was controlled, so as to reduce C-warp. The model and treatment strategies were applied to a leveling unit in China, and the prediction errors of C warping height are controlled within 10%, and the maximum warpage ranges of the unit are reduced from 1.4~6.5 mm to 0.9~4.5 mm. The results show that the C warpage prediction and treatment strategy meet the production demands, and the warpage values are significantly reduced, which reduces the repair rate caused by warpages. Reference | Related Articles | Metrics | Comments（0）

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Study on Thickness Distribution of Single Point Incremental Hydroforming of Complex Shaped Parts SHANG Miao, LI Yan, SHAN Shunkun, YANG Mingshun China Mechanical Engineering    2025, 36 (06): 1338-1344.   DOI: 10.3969/j.issn.1004-132X.2025.06.021 Abstract （1282）      PDF（pc） （10225KB）（77）       Knowledge map    Save To analyze and enhance the performances of single point incremental forming for complex shaped parts, a new process combining SPIF and hydroforming was presented for manufacturing multi-featured parts with spire structures. The forming processes of the target parts were designed, the theoretical prediction model of thickness was established, and the effects of different hydraulic parameters on the thickness distribution of the target parts in different forming stages were analyzed. The experimental results show that complex shaped parts may be formed using the new processes and appropriate hydraulic pressures; the geometric errors between the experimental and theoretical profiles may be reduced from 11.44% to 5.18% with assistance compared to SPIF without hydraulic assistance; the thickness distribution patterns are related to the assisted pressure, forming heights, forming shape, etc., and the established theoretical model may be used to predict the thickness distribution of complex shaped parts. Reference | Related Articles | Metrics | Comments（0）

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Modelling and Optimisation of Dynamic Scheduling in Chinese Materia Medica Pharmaceuticals Workshops Based on Multiple Motivation Drivers ZHAO Peirui1, DENG Chao1, ZHU Bo1, YAN Wenbin1, LIANG Min2, CHEN Min2 China Mechanical Engineering    2025, 36 (06): 1247-1260，1299.   DOI: 10.3969/j.issn.1004-132X.2025.06.012 Abstract （1259）      PDF（pc） （19210KB）（82）       Knowledge map    Save A dynamic scheduling problem of Chinese materia medica pharmaceutical workshop driven by multiple dynamic factors(DSP-CMMPW-MDF) model was established, the multiple dynamic factors such as raw material shortages, emergency order insertions, and machine breakdowns. An improved artificial bee colony with Q-learning(IABC-QL) algorithm was proposed to solve the DSP-CMMPW-MDF with the optimization objective of minimizing makespan. In the IABC-QL algorithm, an opposition-based learning strategy was proposed to generate the initial population, ensuring high quality and diversity of the population individuals. Five local search operations were designed to enhance the deep exploration capability of the algorithm. Thus the proposed model and algorithm were applied to a Chinese materia medica pharmaceutical granule production workshop. The results show that the proposed model may effectively improve the flexibility and adaptability of the production systems in the face of uncertainties. Additionally, a comparison with existing algorithms validates the effectiveness of the proposed algorithm. Reference | Related Articles | Metrics | Comments（0）

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Vehicle Lateral Control Strategy Integrating Road Curvature Feedforward Xinyou LIN, Zhongwei JIN, Yunliang TANG China Mechanical Engineering    2025, 36 (11): 2774-2782.   DOI: 10.3969/j.issn.1004-132X.2025.11.036 Abstract （1238）   HTML （0）    PDF（pc） （2868KB）（59）       Knowledge map    Save Aiming at the problems of low tracking accuracy of autonomous vehicles in the road with large curvature curves， the influences of road curvature on the lateral control strategy were focused， the lateral control strategy was improved and optimized based on the traditional model predictive control（MPC） algorithm from three aspects of vehicle model modeling， yaw stability and time domain optimization， respectively. The road curvature was integrated into the vehicle model， and an error dynamics model with curvature feedforward was established. And then， a lateral control strategy was designed based on curvature feedforward MPC algorithm. Then， a lateral stability constraint consisting of lateral vehicle speed and steady-state lateral angular velocity was added to the strategy to enhance the lateral stability of the vehicles under high curvature conditions. A MAP map was established based on genetic algorithm to optimize the prediction and control time domains of the strategy， taking into account the relationships among vehicle speed， road curvature and time domain. Simulation analysis was conducted， and the results show that the improved lateral control strategy may effectively improve the path tracking precision and lateral stability of the vehicles. Finally， the effectivenesses of the curvature feedforward MPC strategy were verified through real vehicle road tests. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Status and Development Trends of Failure Modes, Effects, and Criticality Analysis for CNC Machine Tool Reliability TIAN Hailong1, 2, SUN Yuzhi1, 2, YANG Zhaojun1, 2, LIU Zhifeng1, 2, CHEN Chuanhai1, 2, HE Jialong1, 2 China Mechanical Engineering    2025, 36 (07): 1430-1441.   DOI: 10.3969/j.issn.1004-132X.2025.07.005 Abstract （1232）      PDF（pc） （1000KB）（144）       Knowledge map    Save FMECA played an important role in reliability maintenance of CNC machine tools. Current researches focused on 4 aspects: comprehensive evaluation of multiple factors, integration of multi-source hierarchical information, integration of multiple analysis methods, and dynamic characteristic modeling. By systematically combination of existing research results, the advantages and existing problems of the 4 aspects were analyzed. Evolution path of machine tool failure modes, effects and criticality analysis were explained by the integration of the characteristics of industrial needs, which provides a theoretical basis for building a high-precision machine tool reliability evaluation system. Reference | Related Articles | Metrics | Comments（0）

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Multicollinearity Parameter Feature Selection for Manufacturing Processes Based on LLEs HU Sheng1, 2, GAO Bingbing1, ZHANG Xi1, LIU Dengji1 China Mechanical Engineering    2025, 36 (06): 1238-1246.   DOI: 10.3969/j.issn.1004-132X.2025.06.011 Abstract （1181）      PDF（pc） （7980KB）（68）       Knowledge map    Save In manufacturing processes, a large number of parameters were easily caused to have multicollinearity, which led to problems such as inaccurate prediction of quality indicators. To address these problems, a feature selection method for multicollinear parameters in the manufacturing processes was proposed based on LLE. Firstly, the multicollinearity of the manufacturing process parameters was diagnosed, and then the multicollinearity was eliminated by using the least absolute shrinkage and selection operator(LASSO) regression. Secondly, the LLE algorithm was used to perform feature selection on the parameters after LASSO regression to obtain independent feature spaces, and they were input into the whale optimization algorithm-support vector machine(WOA-SVM) model to verify the parameter feature selection effectiveness of the proposed algorithm. Finally, the effectiveness of the proposed method was verified through case analysis. The results show that compared with the original data, the proposed method may obtain more accurate prediction results under a lower-dimensional feature space, the correlation coefficient value is up to 0.9702, and the accuracy of feature selection increases by 24.989%. Reference | Related Articles | Metrics | Comments（0）

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Research on Passive Compliance Control Method of High Altitude Wind Turbine Blade Grinding Robots Based on Improved ADRC Hao LI, Xinrong LIU, Yiqin LIU, Diqing FAN China Mechanical Engineering    2025, 36 (08): 1832-1841.   DOI: 10.3969/j.issn.1004-132X.2025.08.018 Abstract （1167）   HTML （6）    PDF（pc） （3159KB）（109）       Knowledge map    Save To cope with unknown disturbances at high altitudes and maintain a constant contact forces at the end of a high-altitude wind turbine blade repair robots during polishing， a passive compliant control algorithm was proposed based on an improved ADRC approach. The algorithm combined dead-zone compensation and gravity compensation algorithms， fully considering issues such as gas compressibility in the pneumatic systems， characteristics of electrical proportional valve dead zones， changes in tilt angle during polishing processes， and unknown disturbances during high-altitude operations.A tracking differentiator was utilized for excessive input signals， a state observer was employed to monitor system disturbances， and compensated through a state error feedback control law. By establishing the mathematical model of the control systems and conducting simulation analysis， it is found that this control algorithm improves both force control performance and response speed compared to the traditional proportional-integral-derivative（PID） algorithm. An experimental platform was constructed to conduct experiments under various operating conditions. The experimental results show that the control algorithm systems achieve 44.6% to 51.4% reductions in settling time， a decrease in the absolute maximum error by 45.4% to 69.4%， and reductions in mean square error by 56.5% to 91.2%. Therefore， this algorithm demonstrates improved dynamic response performance and force control accuracy， along with strong disturbance rejection capabilities and robustness， providing a theoretical foundation for practical engineering applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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High-precision Industrial Robot Teaching Method Based on 6D Light Pens Jihuang LIANG, Weifeng WANG, Haibin WU China Mechanical Engineering    2025, 36 (11): 2710-2719.   DOI: 10.3969/j.issn.1004-132X.2025.11.029 Abstract （1109）   HTML （0）    PDF（pc） （2880KB）（41）       Knowledge map    Save Aiming at the problems of poor precision， sensitivity to light and restricted teaching range in current industrial robot vision teaching methods， a high-precision industrial robot teaching method was proposed based on a 6D light pen. The light pen was equipped with three infrared-emitting marker balls， and infrared binocular camera mounted on the robot's end-effector captured the marker balls to obtain their 3D position coordinates and 3D pose information. Two measures were implemented to enhance the vision teaching precision of the 6D light pens. Firstly， a hand-eye calibration method was proposed based on infrared light-emitting marker balls， which significantly improved the transformation precision from the camera coordinate system to the robot tool coordinate system. Secondly， an adaptive camera tracking method was proposed， allowing the robot to automatically track the position of the light pen through the camera mounted on the end-effector， ensuring that the marker balls remain centered in the camera's field of view， thus effectively improving the teaching precision. Finally， the captured teaching trajectory was transformed into the robot base coordinate system to achieve trajectory programming and tracking. Experimental results show that the maximum single-point error of the light pens is as 0.63 mm， the maximum pose error is as 2.1432°， and the maximum trajectory error is as 0.73 mm. The proposed teaching method may achieve high-precision and high-efficiency teaching for robots. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Multi-objective Trajectory Planning of Manipulators Based on Improved SSA Jianlin LIU, Haisong HUANG, Qingsong FAN, Chi MA, Langlang ZHANG China Mechanical Engineering    2025, 36 (09): 2047-2056.   DOI: 10.3969/j.issn.1004-132X.2025.09.017 Abstract （1103）   HTML （2）    PDF（pc） （5244KB）（140）       Knowledge map    Save To optimize the three objectives of efficiency， energy consumption and impacts at the same time， a multi-objective trajectory planning model was proposed based on an improved SSA. Firstly， the artificial potential field method （APF） was used for path planning to obtain the shortest and collision-free path of the manipulator grasping the materials， and the key motion sequence was extracted to establish a multi-objective function. Then， aiming at the problems of multi-objective salp swarm algorithm （MSSA）， such as poor diversity of initial population， easy to fall into local optimum and slow convergence in solution set space， an improved algorithm namely logistic-sine multi-objective salp swarm algorithm（LMSSA）was proposed. The algorithm combined logistic-sine chaotic mapping， pinhole imaging learning strategy and golden sine development strategy to optimize the control nodes of the seventh-order B-spline curve and complete the multi-objective motion trajectory planning of the robotic arms. Finally， the trajectory planning model was applied to the actual grasping tasks of the manipulator UR16e by building MATLAB-CoppeliaSim-UR16e experimental platform. Experimental results show that based on LMSSA， the manipulator motion planning method realizes the accurate， efficient and energy-saving motion trajectory planning of the manipulator， and is successfully applied to the actual operation scenes. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Tool Wear Monitoring Based on IWOA-IECA-BiLSTM Model BAO Zhenke, CAO Huajun, QIN Fengze, CHEN Zhixiang, TAO Guibao China Mechanical Engineering    2025, 36 (12): 2936-2943.   DOI: 10.3969/j.issn.1004-132X.2025.12.016 Abstract （1087）   HTML （1）    PDF（pc） （2374KB）（44）       Knowledge map    Save To improve the monitoring accuracy of tool wear during machining， a BiLSTM model based on IWOA and IECA mechanism was proposed. Tool wear data segments from the PHM2010 dataset were intercepted， and multi-domain features were extracted. Tool wear strongly correlated features were then obtained by screening with the Pearson correlation coefficient. The input features were used to train the model. The BiLSTM module in the model effectively captured temporal features within the data. The IECA attention mechanism module enhances the feature representational capability. The IWOA module optimized the model's hyperparameters， further improving the model accuracy. The model performance was finally tested based on three-fold cross-validation and compared with several other models. The results demonstrate that the IWOA-IECA-BiLSTM tool wear monitoring model achieves the best performance on most test sets. On test sets C1，C4 and C6， the root mean square error （RMSE） values are as low as 6.5， 12.46， and 9.28， respectively. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Vehicle Yaw and Roll Stability Control Based on Dynamic Output Feedback YIN Xizhi1, 2, 3, HU Sanbao1, 2, 3, FENG Zhiyong1, 2, 3 China Mechanical Engineering    2025, 36 (07): 1453-1462.   DOI: 10.3969/j.issn.1004-132X.2025.07.007 Abstract （1043）      PDF（pc） （3520KB）（66）       Knowledge map    Save In order to improve the yaw and roll stability of in-wheel motor drive electric vehicles under extreme conditions such as high speed and low adhesion, a 3-DOF multi-cell model of vehicle lateral dynamics was established, and a robust layered controller was proposed. The local optimal solution of the upper-level reduced-order dynamic output feedback controller was obtained by iterative search, with the demands for the pole configuration and H∞ performance constraints at the same time. With the optimization objective of minimizing the comprehensive tire load rate, the optimal torque of the lower four wheels was obtained. Simulink and CarSim co-simulation results show that this control strategy may significantly improve vehicle stability under different working conditions, and maintain robustness to system parameter variations and external disturbances.  Reference | Related Articles | Metrics | Comments（0）

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Residual Life Prediction for Bearings Based on Bearing Degradation State Assessment and IGAT-BiGRU Network SONG Lijun, LIU Songlin, XIN Yu, MA Jinghua, XIE Zhengqiu China Mechanical Engineering    2025, 36 (07): 1562-1572.   DOI: 10.3969/j.issn.1004-132X.2025.07.019 Abstract （889）      PDF（pc） （8759KB）（45）       Knowledge map    Save Due to the influences of working conditions and operating conditions, the collected status monitoring data was interfered with strong noise in full life cycle of rolling bearings, and the bearing operating life degradation was nonlinear, which seriously affected the accuracy of residual life prediction. So, a bearing residual life prediction method was proposed based on a joint high-precision FPT degradation state evaluation and an IGAT-BiGRU network, and the XJTU-SY full life cycle bearing dataset was used to verify the effectiveness of the proposed method. The results show that the proposed prediction method may effectively capture the deep spatiotemporal features that characterize the bearing degradation states, and significantly improve the residual life prediction accuracy, compared with methods such as CNN-LSTM. Reference | Related Articles | Metrics | Comments（0）

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Rigid-flexible Coupling Identification of MDOF Excitation for Door Limiter and Shaking Optimization of Window Frame during Closing Chengzhan LI, Pengcheng GUO, Congchang XU, Luoxing LI, Yongfu XIAO, Shuxia JIANG China Mechanical Engineering    2025, 36 (11): 2792-2800.   DOI: 10.3969/j.issn.1004-132X.2025.11.038 Abstract （858）   HTML （2）    PDF（pc） （3581KB）（72）       Knowledge map    Save Aiming at the problems that traditional NVH analysis struggled to accurately extract and predict the dynamic excitation and response of vehicle doors during rotation， a new method was proposed and applied to vibration transfer function analysis based on rigid-flexible coupling for identifying MDOF excitation. Taking the abnormal shaking of the window frame during door closing in a specific vehicle model as the research objective， a whole vehicle rigid-flexible coupling model was established by using multibody dynamics method. The MDOF acceleration dynamic excitation at the limiter installation points was extracted for transfer function analysis. The comparison between simulation and testing results shows that consistent peak values of vibration acceleration are detected at 12 Hz， which verifies the accuracy of the rigid-flexible coupling model. Then， the key factors affecting window frame shaking during door closing were analyzed via simulation， identifying the limiter structure as the core optimization target. An optimization scheme was proposed， and vibration transfer function analysis under the extracted dynamic excitation shows that the optimized limiter structure may significantly reduce the window frame shaking level during door closing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Optimization and Experimental Study of Bolt Retreat Groove Rolling Wheels Based on Finite Element Simulation NIU Yanzhao1, LIU Hongwei1, SONG Yali2, ZHU Xianglong1, HUANG Jiamei2, KANG Renke1 China Mechanical Engineering    2025, 36 (06): 1214-1221.   DOI: 10.3969/j.issn.1004-132X.2025.06.009 Abstract （826）      PDF（pc） （15061KB）（89）       Knowledge map    Save The bolt retreat groove surfaces were susceptible to stress concentration and prone to fatigue failure. To bolster the fatigue resistance of bolts retreat groove surfaces, a specialized rolling tool for the retreat grooves was engineered. The structure of the rolling wheels was optimized based on finite element simulation outcomes of the interaction between the rolling wheel and the bolts retreat grooves. The optimized parameters were utilized to create the rolling tools, and a rolling experiments were carried out. The effectiveness of the rolling wheel parameter optimization was validated by assessing the rolled surface quality, fracture morphology, and fatigue life. The findings indicate that the most favorable residual stress results on the retreat groove surfaces are obtained with a YG8 material rolling wheel with diameter of 60 mm and face angle of 45°. A rolling wheel fillet radius of 0.9 mm produces the deepest residual compressive stress layers, a radius of 1.1 mm yields the highest subsurface residual compressive stress value, and a radius of 1.2 mm generates the maximum surface compressive stress. Trials were conducted with rolling tools featuring three distinct fillet radii, and the extended fatigue life of the bolts is ascertained with a 0.9 mm fillet radius rolling wheels, thereby confirming the optimal configuration of the rolling tools. Reference | Related Articles | Metrics | Comments（0）

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Overview and Prospects of Data-driven Low-carbon Design and Manufacturing of Electromechanical Products WANG Liming, XIAO Xingyuan, LI Fangyi, WANG Xiaoguang, LI Jianfeng, NIE Yanyan, LIU Weitong, LI Liuyuan, WANG Yitong, WANG Boyun, CUI Yuqi China Mechanical Engineering    2026, 37 (4): 764-779.   DOI: 10.3969/j.issn.1004-132X.2026.04.001 Abstract （801）   HTML （2）    PDF（pc） （3560KB）（39）       Knowledge map    Save Carbon footprint data served as the core basis for quantifying the full life-cycle carbon emissions of electromechanical products and driving the low-carbon transformation of the manufacturing industries. Focusing on the whole processes of carbon footprint data from acquisition to application， the relevant research approaches were systematically reviewed. The acquisition technologies for multi-source heterogeneous carbon footprint data and the data quality evaluation system were organized， addressing the question of "how data comes". Focusing on “how to use”， applications of data-driven technologies in low-carbon design and manufacturing were elaborated， including data-based carbon footprint correlation modeling， intelligent prediction， generation of low-carbon design solutions， and multi-objective decision-making methods， as well as data-driven manufacturing energy consumption prediction， low-carbon process planning， and intelligent workshop scheduling strategies. Finally， challenges and future directions for data integrity and system integration in low-carbon manufacturing were discussed， offering theoretical references for the green and low-carbon development of electromechanical products. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Construction Techniques for Wind Power Equipment Contextual Knowledge Graphs SHI Zhiyuan1, 2, KONG Zhiwei2, CHEN Junzhen3, WANG Shuying3 China Mechanical Engineering    2025, 36 (06): 1206-1213.   DOI: 10.3969/j.issn.1004-132X.2025.06.008 Abstract （751）      PDF（pc） （10201KB）（91）       Knowledge map    Save Traditional methods for constructing knowledge graphs didnt consider the contextual constraints on knowledge, making it challenging to effectively represent the complex associative relationships among vast knowledge in complex electromechanical equipment like wind turbines. This limitation hindered the practical applications of knowledge graphs in the production processes. This paper proposed a method for constructing a context-aware knowledge graph tailored for wind turbine equipment. Initially, the method extracted contextual knowledge, module meta-knowledge, and module instance knowledge generated from project customization. Utilizing the SHACL, an ontology model was constructed, incorporating context paths and attribute value constraints, thereby precisely characterizing and extracting various knowledge types. Furthermore, an ontology parsing-based algorithm is introduced for visualizing contextual knowledge subgraphs. Through the parsing of contextual knowledge classes within the ontology, data observation windows were generated for each contextual class, facilitating the construction of multi-dimensional visualization interactions tailored to specific scenarios. Through practical applications, the proposed method effectively integrates module meta-knowledge with project-specific module instance knowledge, meeting the demands for precise representation and diverse application scenarios in wind turbine equipment knowledge. Reference | Related Articles | Metrics | Comments（0）

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Modeling and Identification of Robot End-payloads Based on Joint Torque Balance GAO Guanbin1, 2, ZHAO Siguo1, 2, LI Yingjie1, 2 China Mechanical Engineering    2025, 36 (06): 1188-1197.   DOI: 10.3969/j.issn.1004-132X.2025.06.006 Abstract （744）      PDF（pc） （8978KB）（115）       Knowledge map    Save  To address the challenges of decoupling center of mass parameters in existing end-payload identification methods and the difficulty of implementation on robots with non-open controllers, a torque-balance-based modeling and identification method was proposed for robot end-payloads. The identifiability conditions of the end-payloads were analyzed under joint torque balance, and identification models for the end-payload mass and center of mass position were established. To further decouple the mass and center of mass parameters, a three-step identification strategy was designed, where the load mass was identified first, followed by the center of mass position in x and y, and finally in z. This strategy effectively eliminated the error terms introduced by the projection of joint torques in the identification models. The efficiency of the proposed method was validated through simulation and experiments. Compared with the built-in identification method of a non-open-source six-degree-of-freedom robot, the average error in mass identification is reduced from 0.103 kg to 0.032 kg, while the average error in center of mass position identification is decreased from 50.25 mm to 4.14 mm. Furthermore, compared with dynamics parameter identification, the mass identification error is reduced from 0.179 kg to 0.083 kg, and the center of mass position error is reduced from 10.13 mm to 4.33 mm. Reference | Related Articles | Metrics | Comments（0）

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Fine-grained Carbon Emission Accounting in Aluminum Casting Production Processes Based on Data Allocation WANG Zhihui, PENG Tao, LIU Weipeng, TANG Renzhong China Mechanical Engineering    2026, 37 (4): 920-928.   DOI: 10.3969/j.issn.1004-132X.2026.04.016 Abstract （738）   HTML （2）    PDF（pc） （1999KB）（9）       Knowledge map    Save A data allocation method that accommodated the heterogeneous on-site data collection capabilities of aluminum casting enterprises was proposed， enabling fine-grained carbon emission accounting at the single aluminum casting level， differentiated by product， model， and batch， without requiring additional investment in data collection infrastructure. According to the granularity and interval of carbon emission-related production data， enterprise data acquisition capabilities were categorized into three levels， and corresponding allocation strategies were developed for each level to derive fine-grained energy and material activity levels for individual aluminum castings. Product carbon emissions were calculated using the emission factor method， and a correction mechanism was established to incorporate the effects of internal scrap recycling and external scrap sales. A case study on aluminum wheel production was conducted to validate the proposed method. The results demonstrate that the method effectively overcomes the bias induced by the constant operating condition assumption inherent in traditional literature-based approaches， thereby improving the accuracy of carbon accounting results. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Fusion of Degradation Feature Information and Remaining Life Prediction for Rolling Bearings ZHANG Jianyu, WANG Liuzhen, XIAO Yong, MA Yanan China Mechanical Engineering    2025, 36 (07): 1553-1561.   DOI: 10.3969/j.issn.1004-132X.2025.07.018 Abstract （734）      PDF（pc） （7450KB）（69）       Knowledge map    Save To address the demands for remaining life prediction of rolling bearings, a prediction model was proposed based on SAE and BiLSTM network. Taking the full-life vibration data of rolling bearings as research object, a degradation index set was constructed by developing a hyperbolic inverse transformation-based health indicator and a frequency-domain harmonic degradation factor. The SAE was employed for feature fusion to extract key features and eliminate redundant information. Meanwhile, the BiLSTM model was utilized to capture temporal dependencies and achieve full-cycle life prediction. Experimental results demonstrate that the proposed model outperforms support vector regression, extreme learning machines, and convolutional neural networks in terms of smaller prediction errors and stronger generalization capabilities. Reference | Related Articles | Metrics | Comments（0）

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Research on Equivalent Models for Dynamic Response Analysis of Self-piercing Rivet Joints QIU Kangbo, SONG Haisheng, ZHANG Shenglan, GUO Haotian, YANG Na, WANG Wenxin China Mechanical Engineering    2026, 37 (4): 913-919.   DOI: 10.3969/j.issn.1004-132X.2026.04.015 Abstract （731）   HTML （3）    PDF（pc） （2573KB）（7）       Knowledge map    Save The vehicle bodies were subjected to dynamic loads arising from complex road conditions during actual operations， current research on SPR primarily focused on static load response analysis， with insufficient attention given to dynamic load response analysis. A dynamic modelling and analysis method for SPR joints was proposed based on a mass-spring system herein. ABAQUS finite element analysis was employed to identify the tensile and shear stiffness parameters of the SPR joints. The connection between the SPR joints and the sheet metal was abstracted as an interaction between mass-spring systems， establishing a three-degree-of-freedom nonlinear dynamic response model for SPR. Finite element simulation was used to validate the SPR dynamic response equivalent model. Research indicates that the SPR three-degree-of-freedom nonlinear dynamic response model enables efficient and accurate prediction of SPR joints dynamic responses. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Optimization Model Construction Method of CNC Milling Energy Efficiency Based on Specific Energy Values and ELM-AdaBoost under Small Samples BAO Hong, YANG Shuo, YAO Hang, LI Yapeng China Mechanical Engineering    2026, 37 (4): 821-830.   DOI: 10.3969/j.issn.1004-132X.2026.04.006 Abstract （704）   HTML （0）    PDF（pc） （2560KB）（19）       Knowledge map    Save Aiming at the problems of high cost of energy efficiency data acquisition in CNC milling processes and low prediction accuracy of traditional CNC milling energy efficiency model under small sample data， an energy efficiency optimization model was proposed based on specific energy values and extreme learning machine（ELM）-adaptive enhancement algorithm（AdaBoost）. The experimental data were obtained through orthogonal experimental design， a mechanism model was constructed based on specific energy value， combined ELM and AdaBoost to form ELM-AdaBoost data model， and finally integrated the energy efficiency prediction model， which might guarantee the prediction accuracy while effectively reduce the model's demands for data volume. The energy efficiency optimization models were established with the objectives of minimum specific energy value and minimum machining costs， and the optimal processing parameters were solved and optimized by non-dominated sorting genetic algorithm Ⅱ and entropy weight-TOPSIS， and the machining experiments were conducted to verify the feasibility of the proposed method. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of Adhesion Characteristics of Novel Negative-pressure Adhesion Wall-climbing Robots DONG Weiguang1, LIU Aihua2, SONG Yifeng2 China Mechanical Engineering    2025, 36 (06): 1198-1205.   DOI: 10.3969/j.issn.1004-132X.2025.06.007 Abstract （703）      PDF（pc） （6528KB）（144）       Knowledge map    Save Addressing the challenge in optimizing the adhesion performance of wall-climbing robots based on negative pressure adhesion method due to the complexity of internal flow fields and difficulties in precise modeling, a flow field modeling method was proposed based on flow rate conservation. According to the structural characteristics of negative pressure adhesion systems of a wall-climbing robot, mathematical models of airflow field in negative pressure adhesion systems were built by combining the laws of thermodynamics and N-S equations using air flow rate as the related factor. Then, key influencing factors of adhesion performance were identified based on the model: sealing ring width, leakage gap height, and centrifugal pump power. The effective adhesion forces were changing with airflow of adhesion systems. Results of the simulation and prototype experiments show that the models constructed herein may accurately reflect the changing rules of adhesion performance, and may provide evidences for the optimization of adhesion performance of wall climbing robots with negative pressure adhesion method. Finally, according to the movement characteristics of the wall-climbing robots， adsorption performance optimization strategy was increasing the rated adsorption force to self gravity ratio while decreasing the effective adsorption force to self gravity ratio. Reference | Related Articles | Metrics | Comments（0）

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Large Language Model-driven Knowledge Modeling Method for Low-carbon Product Design YU Sheng, HE Bin China Mechanical Engineering    2026, 37 (4): 814-820.   DOI: 10.3969/j.issn.1004-132X.2026.04.005 Abstract （700）   HTML （0）    PDF（pc） （3247KB）（11）       Knowledge map    Save To address the challenges of multi-source heterogeneity and semantic complexity in low-carbon product design knowledge， a large language model-driven approach for low-carbon design knowledge modeling was proposed. A domain ontology covering product structure， low-carbon factors， and performance constraints was constructed to achieve semantic hierarchical representation from structural design to performance verification. A data annotation paradigm based on large language models was developed， which enables automated semantic labeling of low-carbon design data through a dual-path collaborative mechanism. A contrastive learning-based knowledge extraction model was designed to enhance BERT’s capability in recognizing semantic boundaries and to improve the semantic encoding of the set prediction networks， thereby achieving accurate extraction of multi-entity and multi-relation information. Experimental results show that the proposed method achieves accuracy， recall， and F1 scores of 84.2%， 82.7%， and 83.4%， respectively， providing an intelligent pathway for semantic modeling of low-carbon design knowledge. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Carbon Emission Prediction and Uncertainty Analysis Method for Machining Processes Driven by Manufacturing Scenarios KONG Lin, ZENG Qingliang, WANG Liming, LI Fangyi, ZHANG Xin, LU Zhenguo, WANG Guijie China Mechanical Engineering    2026, 37 (4): 948-958.   DOI: 10.3969/j.issn.1004-132X.2026.04.019 Abstract （699）   HTML （2）    PDF（pc） （4024KB）（14）       Knowledge map    Save Conventional prediction methods faced challenges such as multi-source heterogeneity and strong parameter uncertainty， so equipment， process， and resource-related factors were integrated to identify and define manufacturing scenarios， enabling the unified representation and description of carbon emission influences. The ensemble mechanism of random forest decision trees was combined with Bayesian adaptive hyperparameter optimization to establish a three-stage prediction framework “feature selection， model training， parameter tuning” for the high-efficiency prediction of carbon emissions. A Monte Carlo-Bayesian optimized random forest approach for uncertainty analysis was developed， where sensitive carbon emission parameters were identified and their impacts were quantified to enhance reliability through targeted parameter optimization. A case study on wind turbine blade machining demonstrated the effectiveness of the proposed method. The results show excellent agreement between predicted and actual carbon emissions. After uncertainty analysis， the coefficient of variation is reduced by 0.0347， significantly improving the reliability of the prediction results and supporting more robust decision-making. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Digital Twin-driven Performance Modeling and Dynamic Optimization Methodology for Precision Milling Machines MEI Shulong, XIE Yang, ZHANG Chaoyong, WU Jianzhao, LIU Jinfeng China Mechanical Engineering    2026, 37 (4): 875-884.   DOI: 10.3969/j.issn.1004-132X.2026.04.012 Abstract （696）   HTML （1）    PDF（pc） （4238KB）（9）       Knowledge map    Save A digital twin-based dynamic multi-objective optimization method for machining processes was proposed herein. By integrating historical machining data with real-time operational data， a digital twin system was established， comprising geometric， physical， behavioral， and rule-based sub-models. This system combined an Optuna-GBR model and an IMORIME to dynamically adjust machining parameters. The cutting force fluctuations were monitored in real time by the digital twin system. When the fluctuations exceeded the adaptive threshold， a dynamic optimization process was triggered， during which a new Pareto solution set was regenerated and the optimal machining parameter combination was determined using the entropy-weighted technique for order preference by similarity to an ideal solution（TOPSIS） method. Experimental validation under actual machining conditions demonstrates that the dynamic optimization method of the digital twin system achieves a 19.99% reduction in spindle energy consumption， a 29.02% reduction in specific cutting energy， and an 11.22% reduction in machining noise. These results indicate a significant improvement in machining efficiency and a remarkable reduction in spindle energy consumption and machining noises. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Eco-design for Additive Manufacturing： Knowledge-driven Framework and Applications WANG Yanan, PENG Tao, XIONG Yi, WANG Liming, TANG Yunlong, TANG Renzhong China Mechanical Engineering    2026, 37 (4): 780-791.   DOI: 10.3969/j.issn.1004-132X.2026.04.002 Abstract （688）   HTML （0）    PDF（pc） （14372KB）（20）       Knowledge map    Save To support designers in developing environmentally sustainable components fabricated by additive manufacturing， the concept of eco-design for additive manufacturing（EcoDfAM） was clarified， and a body of knowledge required in EcoDfAM was constructed. A knowledge-driven EcoDfAM framework was proposed based on knowledge by integrating multiple intelligent technologies. This framework consisted of three layers： knowledge source layer， knowledge model layer， design application layer. Taking the valve body parts of a hydraulic system in the aircraft wing that were printed using the metal powder bed fusion technology as an example， the application analysis and discussion of the proposed framework were carried out. The results show that the framework integrating ontology， machine learning， knowledge graph may effectively integrate the complex multi-domain professional knowledge required by EcoDfAM， and generate eco-design recommendations through flexible reuse of knowledge. This study may be used to develop intelligent design advisor systems， providing appropriate knowledge feedback in different design stages to guide designers， effectively improving the efficiency and quality during the eco-design processes of parts fabricated by additive manufacturing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Reliability Dynamic Prediction Method for Remanufactured Products Based on Data-model Integration and Transfer FENG Yukang, ZHU Shuo, JIANG Zhigang, YAN Wei, ZHANG Hua China Mechanical Engineering    2026, 37 (4): 1007-1015.   DOI: 10.3969/j.issn.1004-132X.2026.04.025 Abstract （683）   HTML （3）    PDF（pc） （2592KB）（11）       Knowledge map    Save To address the problems that the reliability data samples of remanufactured products were scarce， leading to difficulties in accurately predicting their reliability status during service， a dynamic reliability prediction method for remanufactured products was proposed， integrating substance-field degradation data from similar products with model transfer fine-tuning. Firstly， the “physical form” and “field properties” degradation indicators affecting product reliability were analyzed using the substance-field model. Then， a comprehensive degradation index reflecting the multi-dimensional substance-field degradation characteristics of products was constructed using a linear regression model， and a three-stage similarity calculation method was designed to screen and transfer historical degradation data from similar products for sample expansion. Secondly， to address the spatial coupling and temporal dependency characteristics of the historical substance-field degradation data of similar products， a reliability prediction model was established based on a convolutional long short-term memory neural network. Furthermore， the parameters of the prediction model were dynamically adjusted through deep transfer learning techniques to improve the prediction accuracy for the reliability of remanufactured products under personalized service scenarios. Finally， the proposed prediction method was validated using a remanufactured spindle system as a case study， and the coefficient of determination （R²） of the prediction results reache 0.92， which indicates the effectiveness of the method. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Fault Diagnosability Evaluation of Meta Actuation Units Based on SABO-VMD Hongyu GE, Zhan ZHAO, Anxiang GUO, Jiarui SUN China Mechanical Engineering    2025, 36 (08): 1774-1783.   DOI: 10.3969/j.issn.1004-132X.2025.08.013 Abstract （682）   HTML （1）    PDF（pc） （2765KB）（110）       Knowledge map    Save This paper introduced an evaluative approach to gauge the complexity of fault diagnosis within meta actuation units. The methodology commences with the decomposition of fault signals from these units， utilizing a VMD technique refined by SABO. The processes included the applications of a Kurtosis-based criterion to select pertinent intrinsic mode functions （IMFs）， culminating in the creation of a feature vector grounded in envelope entropy. The evaluative task then pivoted on employing Cosine distance as a measure of similarity， recasting the fault diagnosability problems into one of assessing the likeness of vibration signal feature vectors across varying fault conditions. A diagnosability evaluation matrix for the meta actuation units was formulated， which layed the foundation for a diagnostic index. It is concluded with an empirical validation using a worm gear-based meta actuation unit； the findings confirm the method’s efficacy in quantitatively gauging the diagnosability of diverse fault patterns. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Energy Consumption Prediction of Industrial Robots Based on Bayesian Optimized Temporal Convolutional Network XIAO Wei, ZHANG Cong, CHEN Xubing China Mechanical Engineering    2026, 37 (4): 831-836.   DOI: 10.3969/j.issn.1004-132X.2026.04.007 Abstract （657）   HTML （0）    PDF（pc） （11630KB）（9）       Knowledge map    Save To achieve online and efficient prediction of industrial robot energy consumptions， a method was proposed based on Bayesian-optimized TCN. Specifically， TCN was utilized to establish a nonlinear mapping relationship between kinematic parameters and robot energy consumption， which effectively captured the temporal characteristics of energy consumption prediction data. Meanwhile， the Bayesian method was adopted to optimize the hyperparameters in the model， thereby improving the accuracy of the energy consumption prediction model. Ablation experiments and comparative experiments on the IRB 1600-10/145 industrial robots show that， under no-load and 1.5 kg load conditions， the average relative errors of the total energy consumptions of the robot predicted by the proposed method are as 1.04% and 1.78% respectively. These results demonstrate that the proposed method outperforms other commonly used energy consumption prediction models at present. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Pneumatic Atomizer Design and Droplet Characterization for MQL in Metal Cutting YUAN Yaohui, WANG Chengyong, LI Weiqiu, ZHENG Lijuan, YAN Bingjiang China Mechanical Engineering    2026, 37 (4): 837-845.   DOI: 10.3969/j.issn.1004-132X.2026.04.008 Abstract （650）   HTML （0）    PDF（pc） （2295KB）（6）       Knowledge map    Save The atomizer， as the core component of the built-in atomized MQL system， directly affected the atomization efficiency of MQL oil and cutting performance. The atomization mechanism of lubricating oil and the key factors influencing atomizer performances were thoroughly analyzed based on the liquid spray theory herein. A pneumatic atomization-type MQL oil mist atomizer was developed. Through a combination of atomizer performance evaluation methods and experimental research， the coupling mechanism of factors such as throat diameter， suction aperture， and gaps between orifice walls on atomizer performance was clarified. Based on the analysis of the laser spray particle size analyzer， the superior atomization performance of the atomizer with oils of varying viscosities was verified. The particle size number frequency distribution is concentrated between 0.8-10 µm， with the volume frequency distribution peaking at 25 µm and the Sauter mean diameter D32 ranging from 5-12 µm. Table and Figures | Reference | Related Articles | Metrics | Comments（0）