International Journal of Manufacturing Technology and Management (13 papers in press)

Regular Issues

• Transmission error compensation method for small module gears of CNC machine tools based on discretization decoupling calculation  
  by Huiqin Li, Jing Chang, Baofeng Zhang 
  Abstract: In order to improve the stability of gear transmission process and reduce transmission failure rate, a transmission error compensation method based on discretization decoupling calculation was proposed for small module gears of CNC machine tools. Analyze the dynamic changes in the gear transmission process, and set up sensors to collect the gear meshing frequency and transmission meshing frequency. Based on the sensing signal, establish a transmission error analysis model. The discretization 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 2800kW and 3000kW, indicating that this method can effectively achieve compensation for transmission errors.
  Keywords: CNC machine tools; Small module gear; Dynamics; Sensors; Transmission error; Discretization principle; Decoupling calculation; Error compensation.
  DOI: 10.1504/IJMTM.2023.10061700
   
  
• Automatic Fault Diagnosis Method for Mining Hydraulic Support Based on Fuzzy Analysis  
  by Minjie Chen, Rongjin Yang 
  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.
  Keywords: fuzzy analysis; hydraulic support; fault diagnosis; moving average method; fuzzy rule library; artificial bee colony.
  DOI: 10.1504/IJMTM.2023.10062073
   
  
• Fault Information Identification Method of Industrial Production Equipment Based on Industrial internet of things  
  by Yun Yang  
  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.
  Keywords: industrial internet of things; IIoT; industrial production equipment; fault information; wavelet coefficients; sparse encoding; least squares support vector machine.
  DOI: 10.1504/IJMTM.2023.10062074
   
  
• Optimization Method for Human Resource Scheduling in Manufacturing Industry Based on Decision Tree Algorithm  
  by Huan Wang, Hao Wang 
  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 A, which improves the efficiency of labour resource allocation and has a good measurement of employee competency.
  Keywords: decision tree algorithm; manufacturing human resources; scheduling optimisation; employee competency; degree of collaboration; Gini index.
  DOI: 10.1504/IJMTM.2023.10062075
   
  
• Research on automatic planning algorithm of surfacing repair path based on 3D vision technology  
  by Zhonghua Ni, Xinhua Wang 
  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. 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.
  Keywords: 3D vision technology; surfacing; repair the path; automatic planning algorithm; planning distance.
  DOI: 10.1504/IJMTM.2023.10062079
   
  
• A Control Method for Disordered Sorting of Industrial Robots Based on Binocular Stereo Vision  
  by Shudong He 
  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.
  Keywords: binocular stereo vision; unordered sorting; improve the A* algorithm; binocular parallax; depth information.
  DOI: 10.1504/IJMTM.2023.10062083
   
  
• Scheduling method for industrial production processes in cloud manufacturing environment  
  by Yang Zhang, Meng-jun Huang, Chao Xu 
  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. 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; minimise industrial production time, production costs, and transportation scheduling frequency as the scheduling objective function, set production process constraints and production equipment constraints. 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.
  Keywords: cloud manufacturing; industrial production; production process scheduling; objective function; constraints; firefly algorithm.
  DOI: 10.1504/IJMTM.2023.10062084
   
  
• Online recognition method for appearance defects in mechanical parts processing based on machine vision  
  by Qian Meng, Pengfei Shi 
  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.
  Keywords: machine vision; mechanical parts; cosmetic imperfections; online recognition; variable threshold technology; maximum entropy segmentation.
  DOI: 10.1504/IJMTM.2023.10062086
   
  
• Product Design Sketch Defect Detection Method Based on Feature Extraction  
  by Liwei Sun 
  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 grayscale processing is carried out. Secondly, linear transformation of product design sketches expands the grayscale 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%.
  Keywords: feature extraction; product design sketch; defect detection; grayscale; PCA algorithm; defect feature extraction.
  DOI: 10.1504/IJMTM.2023.10062087
   
  
• A Method for Predicting the Remaining Life of Mechanical Equipment in Production Lines Based on Similarity Features  
  by Xiangyang Mei, Tianbiao Yang, Wenyou Gao 
  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.
  Keywords: similarity feature; DTW method; normalisation processing; local similarity; EEMD.
  DOI: 10.1504/IJMTM.2023.10062088
   
  
• How can the Adoption of Industry 4.0 Technologies Drive Innovation in Manufacturing Companies? Evidence from China and Korea  
  by Yongping Zhong, Hee Cheol Moon 
  Abstract: Digital manufacturing can optimize the production process and prompt adaptation to dynamic market changes, thereby enhancing firm performance. This study confirmed that adopting Industry 4.0 technology can positively impact technological competitiveness and knowledge acquisition. Both technological competitiveness and knowledge acquisition play crucial roles in promoting product, process and organizational innovation, ultimately leading to improved firm performance. The results indicated that the adoption of Industry 4.0 technology indirectly influences innovations through enhanced technological competitiveness and knowledge acquisition. More importantly, the findings also provide empirical evidence that the type of country moderated the resulting outcomes of Industry 4.0 technology in the manufacturing industry. This study, therefore, contributes to a deeper understanding of the transformation driven by Industry 4.0 in manufacturing by exploring the resulting outcomes of Industry 4.0 technology in different countries. This insight can guide managers in formulating more effective strategies to bolster innovation and enhance firm performance.
  Keywords: Industry 4.0; innovation; technological competitiveness; knowledge acquisition.
  DOI: 10.1504/IJMTM.2025.10069746
   
  
• Motion Planning of a Mobile Manipulator Based on an Optimised Docking Position and Improved A * Algorithm  
  by Yi Cao, Yao Zhao, Xiang Wu, Ming Qi Tang, Yin Hui Guo 
  Abstract: In response to the significant impact that the docking position of a mobile platform has on the operability of the manipulator arm subsystem and overall energy consumption, the docking position of a mobile manipulator was optimised and the path planning algorithm was improved during this study. First, a novel method of optimising the docking position was developed. This method is based on the operability of the manipulator arm, calculated using the Jacobian matrix, along with a specially developed energy consumption evaluation method designed for mobile manipulator systems. Second, to optimise the path of the mobile manipulator, the A* algorithm was enhanced through the design of a new heuristic search function and the integration of the Floyd algorithm, which increased the smoothness of the generated paths. Finally, the proposed method was validated through comprehensive motion-planning experiments conducted in an indoor environment. These experiments utilised a mobile manipulator system that was composed of a six-degree-of-freedom manipulator arm and a Turtlebot2 mobile robot. This research was designed to simultaneously optimise the docking position and motion path of the mobile manipulator while considering both the operability and energy consumption of the manipulator arm.
  Keywords: mobile manipulator docking position; energy-consumption model; manipulator operability; improved A* algorithm.
  DOI: 10.1504/IJMTM.2025.10071242
   
  

Special Issue on: Big Data and AI for Process Innovation in the Industry 4.0 Era

• Research on total amount prediction of import and export trade data based on combined predicting model  
  by Jianxin Chen, Xiaoke Zhao 
  Abstract: In order to overcome the problems of low index significance, low prediction efficiency and low prediction accuracy in the process of prediction of import and export trade data, a new research method for total amount prediction of import and export trade data based on combined prediction model. Combine neural network prediction method and support vector machine prediction method to build a combined prediction model. Through the back propagation process, it can reduce the error between the expected and the actual output, and modify the network threshold and weight, so as to realize the classification of samples in high-dimensional space in support vector machine, and the two methods are combined to complete the prediction of total import and export trade data. The experimental results show that the significance of the proposed method is close to 100%, the prediction time is within 0.5 min, and the prediction accuracy is more than 90%.
  Keywords: Combination predicting model; Import and export trade; Data predicting; Influencing factors.