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Sensors 2014, 14(11), 21588-21602; doi:10.3390/s141121588

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, China
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Received: 13 October 2014 / Revised: 5 November 2014 / Accepted: 10 November 2014 / Published: 14 November 2014
(This article belongs to the Section Physical Sensors)
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Abstract

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. View Full-Text
Keywords: heterogeneous ensemble learning; tool condition monitoring; stacking; force sensor heterogeneous ensemble learning; tool condition monitoring; stacking; force sensor
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, G.; Yang, Y.; Li, Z. Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model. Sensors 2014, 14, 21588-21602.

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