Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-137
Collaborative Optimization of Micro-Textured Tool Parameters Using Support Vector Machine and Swarm Intelligence Algorithms
1 Instructor, Shandong Huayu
Institute of Technology, 968 Daxue East Road, Decheng District, Dezhou,
Shandong, 253034, China, E-mail: xuejunw1@outlook.com (corresponding
author).
2 Instructor, Shandong Huayu University of Technology, China,
E-mail: fengguo@heu.uu.me
3 Process Engineer, Dezhou Xitai Hydraulic Co., Ltd. Tianqu
Industrial Zone, Decheng District, Dezhou, Shandong, 253000 China,
E-mail: yiranwang23@heu.uu.me
Engineering Management
Received January 27, 2026; revised March 17, 2026; accepted September 20, 2026
Available online September 27, 2026
Abstract: This study puts forward a collaborative optimization model for micro-textured tool parameters that integrates a Support Vector Machine (SVM) with swarm intelligence algorithms. The model aims to identify optimal cutting parameters for micro-textured tools. First, an optimized SVM predicts cutting performance indicators with high accuracy. Then, swarm intelligence algorithms efficiently search for optimal parameter combinations in multi-objective optimization. These parameter combinations are fed back into the SVM prediction model to achieve collaborative optimization. Results from the experiments demonstrate that the model achieves prediction accuracies of 97.8% for cutting force and 97.6% for cutting temperature after training. The model's loss value is reduced to 0.12. In the actual processing, the wear scar’s cross-sectional area is only 1237 μm2 at a load of 40 N when the micro-textured tool with optimal values obtained by the proposed method is used for cutting. Moreover, the wear rate is only 10.02 %. This indicates that the proposed method exhibits greater stability and flexibility than the reference models.
Keywords: Support vector machine (SVM); swarm intelligence algorithms; micro-textured tool; collaborative parameter optimization; multi-objective particle swarm optimization.
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Citation: Wang, X., Guo, F., and Wan, Y. (2026). Collaborative Optimization of Micro-Textured Tool Parameters Using Support Vector Machine and Swarm Intelligence Algorithms. Journal of Engineering, Project, and Production Management, 16(6), 2026-137.
