Template-Type: ReDIF-Article 1.0 Author-Name: Huiqin Li Author-X-Name-First: Huiqin Author-X-Name-Last: Li Author-Name: Jing Chang Author-X-Name-First: Jing Author-X-Name-Last: Chang Author-Name: Baofeng Zhang Author-X-Name-First: Baofeng Author-X-Name-Last: Zhang Title: Transmission error compensation method for small module gears of CNC machine tools based on discretisation decoupling calculation Abstract: In order to improve the stability of gear transmission process and reduce transmission failure rate, a transmission error compensation method based on discretisation decoupling calculation was proposed for small module gears of CNC machine tools. First, analyse the dynamic changes in the gear transmission process, and set up sensors to collect the gear meshing frequency and transmission meshing frequency. Then, based on the sensing signal, establish a transmission error analysis model. The discretisation analysis concept is introduced into the model, and the difference between the theoretical tooth surface and the actual machined tooth surface is taken as the transmission error. After the decoupling calculation, the error compensation amount is obtained. Experiment show that this method can control the gear transmission failure rate between 0.042 and 0.065, and maintain the transmission output power between 2,800 kW and 3,000 kW, indicating that this method can effectively achieve compensation for transmission errors. Journal: Int. J. of Manufacturing Technology and Management Pages: 92-109 Issue: 1/2 Volume: 40 Year: 2026 Keywords: CNC machine tools; small module gear; dynamics; sensors; transmission error; discretisation principle; decoupling calculation; error compensation. File-URL: http://www.inderscience.com/link.php?id=151514 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:92-109 Template-Type: ReDIF-Article 1.0 Author-Name: Minjie Chen Author-X-Name-First: Minjie Author-X-Name-Last: Chen Author-Name: Rongjin Yang Author-X-Name-First: Rongjin Author-X-Name-Last: Yang Title: Automatic fault diagnosis method for mining hydraulic support based on fuzzy analysis Abstract: A fuzzy analysis based automatic fault diagnosis method for mining hydraulic supports is proposed with the goal of improving fault detection rate, reducing misdiagnosis rate, and improving diagnostic efficiency. Firstly, for the structure of mining hydraulic support, real-time data collection of mining hydraulic support work is carried out through multiple DS18B20 sensor arrays. Then, the moving average method is used to denoise the collected data, and the artificial bee colony algorithm is used to extract fault features. Finally, based on the results of data denoising and feature extraction, a fuzzy rule library is established using expert experience and knowledge, and fault diagnosis is achieved through fuzzy reasoning and deblurring. The experimental results show that the highest fault detection rate of the proposed method is 83.5%, and the misdiagnosis rate remains below 2%, with a relatively short fault diagnosis time. Journal: Int. J. of Manufacturing Technology and Management Pages: 78-91 Issue: 1/2 Volume: 40 Year: 2026 Keywords: fuzzy analysis; hydraulic support; fault diagnosis; moving average method; fuzzy rule library; artificial bee colony. File-URL: http://www.inderscience.com/link.php?id=151515 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:78-91 Template-Type: ReDIF-Article 1.0 Author-Name: Yun Yang Author-X-Name-First: Yun Author-X-Name-Last: Yang Title: Fault information identification method of industrial production equipment based on industrial internet of things Abstract: In order to solve the problems of low recognition accuracy and long recognition time existing in the existing fault information recognition methods for industrial production equipment, this paper proposes a fault information recognition method for industrial production equipment based on the industrial internet of things. First, based on the industrial internet of things technology, build an information collection platform for industrial production equipment. Then, based on wavelet coefficients, the equipment signal is pre-processed and the fault features of industrial production equipment are extracted based on sparse expression. Finally, a least squares support vector model is constructed to classify fault signals and achieve recognition of industrial production equipment fault information. Through experiments, it can be seen that the accuracy of using the method proposed in this article for recognition is always above 96%, and the recognition time is always within 7.50 s, which has good recognition effect and efficiency. Journal: Int. J. of Manufacturing Technology and Management Pages: 110-124 Issue: 1/2 Volume: 40 Year: 2026 Keywords: industrial internet of things; IIoT; industrial production equipment; fault information; wavelet coefficients; sparse encoding; least squares support vector machine. File-URL: http://www.inderscience.com/link.php?id=151516 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:110-124 Template-Type: ReDIF-Article 1.0 Author-Name: Huan Wang Author-X-Name-First: Huan Author-X-Name-Last: Wang Author-Name: Hao Wang Author-X-Name-First: Hao Author-X-Name-Last: Wang Title: Optimisation method for human resource scheduling in manufacturing industry based on decision tree algorithm Abstract: To overcome the problems of low efficiency in traditional labour resource allocation and poor measurement of employee competence, this paper designs a manufacturing human resource scheduling optimisation method based on decision tree algorithm. Firstly, determine the principles of resource scheduling optimisation and obtain scheduling optimisation parameters. Then, using entropy calculation method to calculate the importance of parameters, establish a decision tree to obtain a classification scheduling optimisation rule set, and sort the parameter attributes. Finally, the Gini index is used to classify the sample parameters and construct a decision tree for resource scheduling optimisation, achieving the final scheduling optimisation. The results show that the overall allocation efficiency under this method is higher than 98%, and the competency measurement level is all <i>A</i>, which improves the efficiency of labour resource allocation and has a good measurement of employee competency. Journal: Int. J. of Manufacturing Technology and Management Pages: 60-77 Issue: 1/2 Volume: 40 Year: 2026 Keywords: decision tree algorithm; manufacturing human resources; scheduling optimisation; employee competency; degree of collaboration; Gini index. File-URL: http://www.inderscience.com/link.php?id=151517 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:60-77 Template-Type: ReDIF-Article 1.0 Author-Name: Zhonghua Ni Author-X-Name-First: Zhonghua Author-X-Name-Last: Ni Author-Name: Xinhua Wang Author-X-Name-First: Xinhua Author-X-Name-Last: Wang Title: Research on automatic planning algorithm of surfacing repair path based on 3D vision technology Abstract: To solve the problems existing in the traditional automatic planning algorithm of surfacing repair path, an automatic planning algorithm of surfacing repair path based on 3D vision technology is proposed. Firstly, the one-way planning distance is calculated, the pixel value containing weld in the target image is set to 0 based on 3D vision technology, the threshold value of weld is calculated, the surfacing repair path planning target is constructed, and the planning nodes are set. The, calculate the displacement value of the component that needs welding deformation, and get the automatic planning result of surfacing repair path. The experimental results show that the target location result obtained by the proposed algorithm is more accurate, and the characteristics of the weld test sample image can be accurately planned. The average root mean square error after the supplement is smaller, which is 0.1 cm, indicating that the proposed method can effectively improve the path planning effect. Journal: Int. J. of Manufacturing Technology and Management Pages: 30-43 Issue: 1/2 Volume: 40 Year: 2026 Keywords: 3D vision technology; surfacing; repair the path; automatic planning algorithm; planning distance. File-URL: http://www.inderscience.com/link.php?id=151518 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:30-43 Template-Type: ReDIF-Article 1.0 Author-Name: Shudong He Author-X-Name-First: Shudong Author-X-Name-Last: He Title: A control method for disordered sorting of industrial robots based on binocular stereo vision Abstract: Controlling the disorderly sorting of industrial robots can significantly improve sorting efficiency, reduce costs, and improve production efficiency. Therefore, this article proposes a method for disorderly sorting control of industrial robots based on binocular stereo vision. Firstly, the binocular stereo vision method is used to calibrate the sorting items of industrial robots. Secondly, the binocular parallax principle is used to obtain the depth information of the selected items and extract the pose of the sorting target object. Then, the rotation method is used to extract the minimum bounding rectangle, and the motion equation of the object to be sorted and the end of the robot is constructed. Finally, implement unordered sorting control based on machine vision. The results show that the calibration error of the proposed method is only 1.6%, and the control accuracy is as high as 96.2%, indicating that the proposed method can effectively improve the sorting effect. Journal: Int. J. of Manufacturing Technology and Management Pages: 44-59 Issue: 1/2 Volume: 40 Year: 2026 Keywords: binocular stereo vision; unordered sorting; improve the A* algorithm; binocular parallax; depth information. File-URL: http://www.inderscience.com/link.php?id=151519 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:44-59 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Zhang Author-X-Name-First: Yang Author-X-Name-Last: Zhang Author-Name: Meng-Jun Huang Author-X-Name-First: Meng-Jun Author-X-Name-Last: Huang Author-Name: Chao Xu Author-X-Name-First: Chao Author-X-Name-Last: Xu Title: Scheduling method for industrial production processes in cloud manufacturing environment Abstract: A method for scheduling industrial production processes in a cloud manufacturing environment is proposed with the aim of improving the scheduling effectiveness of industrial production processes. First, establish a cloud manufacturing platform architecture, comprehensively consider industrial production tasks, processes, loading and unloading, and transportation links, and establish a scheduling framework in the cloud manufacturing environment. Then, minimise industrial production time, production costs, and transportation scheduling frequency as the scheduling objective function, and set production process constraints and production equipment constraints. Finally, optimise the attraction term and random term of the standard firefly algorithm, and continuously optimise the objective function through multiple iterations to obtain the optimal scheduling result. The experimental results indicate that the production cost, transportation scheduling frequency, and industrial production time of this method are relatively low, indicating its feasibility. Journal: Int. J. of Manufacturing Technology and Management Pages: 15-29 Issue: 1/2 Volume: 40 Year: 2026 Keywords: cloud manufacturing; industrial production; production process scheduling; objective function; constraints; firefly algorithm. File-URL: http://www.inderscience.com/link.php?id=151520 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:15-29 Template-Type: ReDIF-Article 1.0 Author-Name: Qian Meng Author-X-Name-First: Qian Author-X-Name-Last: Meng Author-Name: Pengfei Shi Author-X-Name-First: Pengfei Author-X-Name-Last: Shi Title: Online recognition method for appearance defects in mechanical parts processing based on machine vision Abstract: In order to solve the shortcomings of traditional parts appearance defect recognition methods with low recognition accuracy and long recognition time, this paper proposes an online recognition method for mechanical parts processing appearance defects based on machine vision. Firstly, the image acquisition environment of mechanical parts based on machine vision is determined. Secondly, the part image is pre-processed; thirdly, the maximum entropy segmentation method is used to complete the image segmentation. Finally, the defect texture features of the part image are extracted, and the support vector machine algorithm is combined to realise the online recognition of the appearance defects of mechanical parts. Experiments show that the recognition time of the proposed method never exceeds 600 s, the recognition accuracy is 93.75%, and the average time overhead of identifying a part is 0.5 s, which has high recognition accuracy and less time overhead, and has better application performance. Journal: Int. J. of Manufacturing Technology and Management Pages: 138-153 Issue: 1/2 Volume: 40 Year: 2026 Keywords: machine vision; mechanical parts; cosmetic imperfections; online recognition; variable threshold technology; maximum entropy segmentation. File-URL: http://www.inderscience.com/link.php?id=151521 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:138-153 Template-Type: ReDIF-Article 1.0 Author-Name: Liwei Sun Author-X-Name-First: Liwei Author-X-Name-Last: Sun Title: Product design sketch defect detection method based on feature extraction Abstract: There are problems with high false alarm rates and low registration of defect features in product design sketch defect detection. To this end, a defect detection method for product design sketches based on feature extraction is designed. First, the Gaussian filter method is introduced to remove the noise in the product design sketch, and greyscale processing is carried out. Secondly, linear transformation of product design sketches expands the greyscale range of sketch defects and changes the local enhancement effect. Finally, through the PCA algorithm in the feature extraction algorithm, the defect features of the product design sketch are extracted, and the product design sketch with this feature is regarded as an image with defects. The results show that the proposed method can effectively reduce the false alarm rate in detection and improve the registration degree of defect features, with a maximum registration degree of about 99%. Journal: Int. J. of Manufacturing Technology and Management Pages: 1-14 Issue: 1/2 Volume: 40 Year: 2026 Keywords: feature extraction; product design sketch; defect detection; greyscale; PCA algorithm; defect feature extraction. File-URL: http://www.inderscience.com/link.php?id=151522 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: Xiangyang Mei Author-X-Name-First: Xiangyang Author-X-Name-Last: Mei Author-Name: Tianbiao Yang Author-X-Name-First: Tianbiao Author-X-Name-Last: Yang Author-Name: Wenyou Gao Author-X-Name-First: Wenyou Author-X-Name-Last: Gao Title: A method for predicting the remaining life of mechanical equipment in production lines based on similarity features Abstract: In order to shorten the time required for predicting the remaining life of mechanical equipment and reduce prediction errors, this paper proposes a method for predicting the remaining life of mechanical equipment in production lines based on similarity features. Firstly, obtain the vibration signals of mechanical equipment in the production line and extract signal features; then, calculate the similarity characteristics between the degradation indicators of mechanical equipment. Finally, the DTW method is used to measure the similarity of the overall lifespan of mechanical equipment, and the final remaining lifespan of the predicted samples is calculated based on the actual remaining lifespan of each reference sample and corresponding weights, achieving residual lifespan prediction. The results show that the prediction time of our method is only four seconds, and the prediction error does not exceed 8.33%, which verifies the effectiveness of our method. Journal: Int. J. of Manufacturing Technology and Management Pages: 125-137 Issue: 1/2 Volume: 40 Year: 2026 Keywords: similarity feature; DTW method; normalisation processing; local similarity; EEMD. File-URL: http://www.inderscience.com/link.php?id=151523 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:125-137