Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model
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 in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/11/21588 |
| _version_ | 1818013732061577216 |
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| author | Guofeng Wang Yinwei Yang Zhimeng Li |
| author_facet | Guofeng Wang Yinwei Yang Zhimeng Li |
| author_sort | Guofeng Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:36:56Z |
| format | Article |
| id | doaj.art-eda4675d741e445da9ea00922838e071 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:36:56Z |
| publishDate | 2014-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eda4675d741e445da9ea00922838e0712022-12-22T02:07:26ZengMDPI AGSensors1424-82202014-11-011411215882160210.3390/s141121588s141121588Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning ModelGuofeng Wang0Yinwei Yang1Zhimeng Li2Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaTool 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.http://www.mdpi.com/1424-8220/14/11/21588heterogeneous ensemble learningtool condition monitoringstackingforce sensor |
| spellingShingle | Guofeng Wang Yinwei Yang Zhimeng Li Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model Sensors heterogeneous ensemble learning tool condition monitoring stacking force sensor |
| title | Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model |
| title_full | Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model |
| title_fullStr | Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model |
| title_full_unstemmed | Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model |
| title_short | Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model |
| title_sort | force sensor based tool condition monitoring using a heterogeneous ensemble learning model |
| topic | heterogeneous ensemble learning tool condition monitoring stacking force sensor |
| url | http://www.mdpi.com/1424-8220/14/11/21588 |
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