Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression

Abstract

Cutting tool plays a critical role in modern manufacturing system and tool wear prediction has a great effect on product cost and quality. Many efforts have been devoted to developing tool wear prediction models. The previous studies mainly focus on utilizing a single model during the whole prediction process named global model and thus ignore the local characteristics of different wear stages. However, paying no attention to the impact of wear stage division may lead to mismatch the distribution characteristics of datasets and overlapping of feature information, failing to get the desired accuracy. To achieve more accurate prediction results, this paper proposes a wear stage division-based tool wear prediction method (WSDTWP) based on the improved symmetrized dot pattern (ISDP) and multi-covariance Gaussian process regression (MCGPR). Firstly, according to the varying trend of tool wear value, tool wear process is divided into three stages, including initial wear stage, moderate wear stage and severe wear stage. Then, SDP technique reconstructs the original signals visually and the main SDP parameters are adaptively selected by developing an evaluation model. Further, to deal with the issue of high-dimension and small-size datasets in initial and severe wear stages, MCGPR is developed and hyperparameters are optimized by particle swarm optimization for accurately predicting tool wear. According to the varying prediction requirements, different approaches are assigned into different wear stages to achieve better performance. Finally, three cutting tests are conducted to validate the effectiveness of the proposed approach. The experimental results indicate that WSDTWP is more accurate than existing methods, which provides new theoretical and practical support for identifying tool working condition and predicting tool wear.