An intelligent prediction model of the tool wear based on machine learning in turning high strength steel. (November 2020)

• Record Type: Journal Article
• Authors: Cheng, Minghui; Jiao, Li; Shi, Xuechun; Wang, Xibin; Yan, Pei; Li, Yongping
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 13(2020)
• Page Start: 1580
• Page End: 1597
• Publication Date: 2020-11
• Subjects: Tool wear -- tool condition monitoring -- gray level co-occurrence matrix -- support vector regression; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405420935787
• Languages: English
• ISSNs: 0954-4054

Abstract

In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.