Feature selection for predicting tool wear of machine tools
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ORIGINAL ARTICLE
Feature selection for predicting tool wear of machine tools Wen-Nan Cheng 1 & Chih-Chun Cheng 1
&
Yao-Hsuan Lei 1 & Ping-Chun Tsai 1
Received: 27 May 2020 / Accepted: 20 September 2020 / Published online: 13 October 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model. First, the features extracted from vibration signals were ranked according to their contributions to tool wear prediction. The features were then filtered through a screening process based on singular value decomposition to eliminate redundant features, which exhibited collinearity with features of higher rankings. The aim of the aforementioned steps was to use a relatively small number of highly appropriate features to create an accurate real-time tool wear prediction model. The results indicated that the accuracy of the tool wear prediction model based on the proposed feature ranking and screening methodology is higher than that of models without feature ranking or screening. Moreover, the proposed feature was proven to be more important and effective than other features. Keywords Feature ranking . Feature screening . Singular value decomposition . Tool wear
1 Introduction The success of a machining process is determined by several criteria, such as the quality of the workpiece and the machining efficiency. The workpiece should have an acceptable dimensional accuracy and surface roughness. Moreover, high machining efficiency can be achieved by ensuring a high material removal rate (MRR), long cutting tool life, and low energy consumption. The cutting tool plays a crucial role in fulfilling the aforementioned requirements. For example, although a high MRR is the main objective in rough cutting, tool wear should be minimized to reduce cost. Tool wear is a crucial factor that influences the workpiece surface roughness during the finishing process. A workpiece that is produced inefficiently or has poor surface quality causes major costrelated problems, especially in aerospace manufacturing, Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00170-020-06129-5) contains supplementary material, which is available to authorized users. * Chih-Chun Cheng [email protected] 1
Advanced Institute of Manufacturing with High-tech Innovations, And Department of Mechanical Engineering, National Chung Cheng University, No.168, Sec. 1, University Rd., 621 Min-Hsiung Township, Chia-Yi County, Taiwan (Republic of China)
where the workpiece is composed of expensive heatresistant alloys, such as Inconel 718. Consequently, cutting Tool Condition Monitoring (TCM) is essential during the machining process to ensure consistent product quality and accurately predict the Remainin
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