Research on Milling Force Prediction Model Based on Improved Particle Swarm Optimization Algorithm

被引：1
|
作者：
Liu Ling [1 ]
Qi Weiwei [1 ]
Liu Tingting [1 ]
机构：
[1] Tianjin Univ Technol & Educ, Sch Informat Technol Engn, Tianjin 300222, Peoples R China
来源：
2018 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS AND CONTROL ENGINEERING (ISPECE 2018) | 2019年 / 1187卷
关键词：
D O I：
10.1088/1742-6596/1187/3/032093
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
According to the remarkable characteristics of milling force, an innovative method of milling force modeling using improved particle swarm optimization (PSO) fuzzy system based on support vector machine (SVM) is proposed in this paper. The experiment of titanium alloy milling is designed and implemented. The advanced tester is used to measure the milling force. The training data and test data based on the fuzzy system are obtained. The gradient descent algorithm is embedded in the ordinary particle swarm optimization algorithm to obtain the improved particle swarm optimization algorithm. The convergence effect of the improved particle swarm optimization algorithm is obviously better than that of the ordinary particle swarm optimization algorithm. The improved particle swarm optimization (IPSO) based on fuzzy system is applied to the milling force modeling. Finally, the improved particle swarm optimization (PSO), gradient descent algorithm and improved particle swarm optimization (IPSO) are used to train the fuzzy system, and the conclusion that the final training error of the improved particle swarm optimization (IPSO) is the smallest is obtained.
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页数：5
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