Tool wear predictive model based on least squares support vector machines

Abstract

The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear predictive model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW. PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed predictive model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials.