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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 （1755）      PDF（pc） （6423KB）（339）       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 （1621）   HTML （15）    PDF（pc） （1816KB）（92）       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 （1523）   HTML （2）    PDF（pc） （3435KB）（120）       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 （1516）   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 （1510）   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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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 （1499）      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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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 （1498）      PDF（pc） （6886KB）（129）       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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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 （1425）      PDF（pc） （9879KB）（101）       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 （1297）   HTML （0）    PDF（pc） （2976KB）（64）       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 （1290）      PDF（pc） （1401KB）（68）       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 （1286）      PDF（pc） （10225KB）（80）       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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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 （1275）   HTML （3）    PDF（pc） （3560KB）（49）       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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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 （1261）      PDF（pc） （19210KB）（85）       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 （1242）   HTML （0）    PDF（pc） （2868KB）（61）       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 （1237）      PDF（pc） （1000KB）（145）       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 （1182）      PDF（pc） （7980KB）（71）       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 （1168）   HTML （6）    PDF（pc） （3159KB）（110）       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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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 （1114）   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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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 （1111）   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 （1108）   HTML （3）    PDF（pc） （5244KB）（141）       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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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 （1095）   HTML （3）    PDF（pc） （4238KB）（13）       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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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 （1090）   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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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 （1074）   HTML （2）    PDF（pc） （2560KB）（21）       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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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 （1062）   HTML （0）    PDF（pc） （3247KB）（13）       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 （1057）   HTML （2）    PDF（pc） （4024KB）（15）       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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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 （1044）   HTML （3）    PDF（pc） （2592KB）（14）       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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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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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 （998）   HTML （0）    PDF（pc） （14372KB）（33）       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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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 （992）   HTML （1）    PDF（pc） （11630KB）（11）       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 （972）   HTML （1）    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）

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Experimental Investigation of Ultrasonically-assisted Energy-saving Surface Drying Techniques for Wire Materials GU Wenting, CHEN Xiuhong, FENG Shaoke, YAN Lutao, GAO Yaxiang China Mechanical Engineering    2026, 37 (4): 866-874.   DOI: 10.3969/j.issn.1004-132X.2026.04.011 Abstract （944）   HTML （2）    PDF（pc） （3396KB）（10）       Knowledge map    Save Conventional wire drying typically relied on high-speed air-jet blowing， which had issues such as extremely thin liquid films on the wire surfaces and a small area affected by the airflow. Applying ultrasonic energy directly to the wire structures so that the solid-liquid interface underwent high-frequency vibration and an ultrasonic atomization effect was produced. An energy-consumption model coupling ultrasonic atomization and high-speed air jets was developed， and comparative experiments were carried out focusing on key parameters such as ultrasonic power and droplet volume. Results show that ultrasound induces rapid droplet spreading and atomization via capillary waves and acoustic cavitation， and the liquid-film removal efficiency is markedly superior to that of air jets. At a droplet volume of 10 \mathrm{\mu }\mathrm{L} and an exposure time of 18 ms， 360 W ultrasound achieves a removal rate of 84.37%， whereas a 450 W high-speed air jet removed only 8.58% of the film. In terms of energy consumption， under conditions producing equivalent removal， ultrasound reduces system energy use by at least approximately 46.7% compared with air jets. This paper demonstrates the feasibility of ultrasonic-assisted rapid， energy-efficient dewatering and drying of wire surfaces. Table and Figures | 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 （908）   HTML （4）    PDF（pc） （2573KB）（8）       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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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 （892）      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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Decision-making of Disassembly Sequence Alternatives for End-of-life Mechanical Products Based on Intuitionistic Normal Cloud ZHANG Honghao, HAN Tao, ZHANG Shaolin, WANG Danqi, TIAN Guangdong, HUANG Zhongwei China Mechanical Engineering    2026, 37 (4): 999-1006.   DOI: 10.3969/j.issn.1004-132X.2026.04.024 Abstract （890）   HTML （2）    PDF（pc） （5447KB）（11）       Knowledge map    Save To address the challenges of disassembly end-of-life mechanical products in complex and fuzzy environments， which complicated the sequencing of disassembly alternatives， a hybrid multi-criteria decision-making method was proposed based on INC fuzzy theory and incorporating integrated subjective and objective weights. According to dismantling features， a comprehensive optimization criteria system for evaluating disassembly sequence alternatives of end-of-life mechanical products was constructed across the environmental， economic， and human-related dimensions. Combining Monte Carlo simulation with cloud model distance， improvements were made to the cross-entropy and full consistency method to determine criteria fusion weights. The INC-measurement of alternatives and ranking according to compromise solution method was subsequently employed to rank the disassembly sequence alternatives and determine the optimal solution. Finally， an end-of-life coal-fire power products was employed as a case study to verify the scientific rigor and effectiveness of the proposed method by comparing the impacts of different weighting strategies and decision-making methods on the final results. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Interpretable Modeling and Optimization of Laser Hardening Process Parameters for QT550-5 LIANG Qiang, CHEN Hong, ZHENG Yinpeng, WANG Bing, DU Yanbin, LONG Shuai China Mechanical Engineering    2026, 37 (4): 900-912.   DOI: 10.3969/j.issn.1004-132X.2026.04.014 Abstract （880）   HTML （2）    PDF（pc） （7418KB）（15）       Knowledge map    Save Aimed to achieve laser surface hardening and optimize processing parameters for nodular cast iron QT550-5， a finite element model coupling the temperature and phase transformation fields was developed herein. Using laser power， scanning speed， and overlap rate as experimental variables， and targeting the hardened layer depth and molten layer depth as optimization objectives， Latin hypercube sampling was first employed for the experimental design. A Bayesian-optimized multi-task neural network prediction model was constructed based on the experimental data. SHAP were introduced for interpretability analysis to clarify the contribution mechanism of various parameters to the hardening results. Subsequently， the multi-objective hippopotamus optimization algorithm was used for parameter optimization. A comprehensive evaluation system integrating the entropy weight method and the technique for order preference by similarity to ideal solution was established to rank the non-dominated solution set and determine the optimal parameter combination. Experimental validation under the optimal parameters confirmes the significant surface hardening effectiveness in QT550-5. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Customer-benefit-oriented Energy-efficient Scheduling of Machine Tool Service Resources in Cloud Manufacturing Based on Non-cooperative Game ZHOU Lirong, CHEN Zihan, WANG Guangcun, KONG Lin, ZENG Guiyuan, REN Yayun China Mechanical Engineering    2026, 37 (4): 855-865.   DOI: 10.3969/j.issn.1004-132X.2026.04.010 Abstract （879）   HTML （1）    PDF（pc） （3157KB）（8）       Knowledge map    Save To solve the competition for machine tool resources and conflicts of customer benefits in cloud manufacturing， and to promote the balanced conversion of manufacturing service energy efficiency into customer benefits， a non-cooperative game-based energy-efficient scheduling method for machine tool resources in cloud manufacturing was proposed. a customer-benefit-oriented energy-efficient scheduling model for cloud manufacturing machine tools was constructed by classifying customer preferences into five types： time-sensitive， energy-efficient， cost-effective， quality-focused， and comprehensive. An improved non-cooperative game genetic algorithm was employed to solve the Nash equilibrium. The impact trends of differentiated discount strategies on scheduling outcomes were analyzed based on customer value classification. Simulation experiments conducted on the manufacturing of multiple cylinder piston rod parts for typical construction machinery products from Xuzhou Construction Machinery Group（XCMG） demonstrate that the proposed method increases the average customer manufacturing task benefit by 4.1% and reduces task energy consumption by up to 28%. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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GraphRAG-based Disassembly Sequences Planning for End-of-life Power Batteries WANG Hang, YAN Wei, ZHANG Xumei, ZHU Shuo, JIANG Zhigang, ZHU Zerui China Mechanical Engineering    2026, 37 (4): 967-976.   DOI: 10.3969/j.issn.1004-132X.2026.04.021 Abstract （869）   HTML （3）    PDF（pc） （3829KB）（11）       Knowledge map    Save To address the challenges of low-efficiency disassembly sequence generation caused by the lack of knowledge reusability of end-of-life power batteries， a disassembly sequence planning method was proposed based on GraphRAG， by integrating the complementary strengths of KGs in structured knowledge representation and LLMs in semantic reasoning. First， a KG label-matching-based subgraph generation method was proposed， utilizing the Cypher query language to form custom disassembly sequence subgraphs for specific battery models. Second， a coarse-grained disassembly knowledge retrieval mechanism employing hybrid retrieval and re-ranking was established to locate target battery components precisely. Finally， a fine-grained retrieval model of disassembly knowledge was constructed based on multi-hop reasoning of hierarchical constraint relations. By extracting the disassembly sequence information associated with the components， the intelligent generation of disassembly sequences was achieved using the large language model. The experimental results indicate that the proposed method achieves an accuracy of 93.9% in disassembly sequence generation across five mainstream power batteries， demonstrating the excellent feasibility and effectiveness. Table and Figures | 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 （861）   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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Research Progresses on Synergistic Technology of Micro-textured and Minimum Quantity Lubrication on Tool Surfaces NIU Qiulin, ZHU Chenyi, WU Binghui, ZHANG Shengfeng, GUO Tao, GAO Jingyi China Mechanical Engineering    2026, 37 (4): 885-899.   DOI: 10.3969/j.issn.1004-132X.2026.04.013 Abstract （859）   HTML （2）    PDF（pc） （5353KB）（6）       Knowledge map    Save This paper systematically reviewed the research progresses of MQL and micro-textured tool technology in DMMs cutting in recent years， and focused on analyzing the synergistic mechanism and the influences on DMMs cutting performance. Compared with dry cutting， the synergistic effects of micro-textured+MQL may reduce the average cutting force by 25% to 48.3%， reduce the cutting temperature by 20% to 50%， improve the surface roughness by 15% to 40%， and extend the tool life by 1.5 to 2.4 times. The existing research is yet to be strengthened in the following aspects： first， quantitative theoretical model of synergistic mechanism； second， adaptability analysis for different materials； and third， experimental verification of long-term stability under industrialized conditions. Future research should focus on the construction of quantitative synergistic models based on thermodynamic and tribological behaviors， especially the optimization of the applications in the machining of high-strength and high-temperature materials such as titanium-based alloys and aluminum-based alloys， and the verification of the industrial application value through the evaluation of the long-term performance under actual working conditions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）