Application of genetic programming in the identification of tool wear

Purpose The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear. Design/methodology/approach In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is use...

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Veröffentlicht in:	Engineering computations Jg. 38; H. 6; S. 2900 - 2920
Hauptverfasser:	Wang, Hao, Dong, Guangming, Chen, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bradford Emerald Publishing Limited 09.07.2021 Emerald Group Publishing Limited
Schlagworte:	Acoustic emission Algorithms Fault diagnosis Genetic algorithms Machine learning Mutation Neural networks Polynomials Population Regression models Signal processing Support vector machines Tool wear Support vector regression Identification Detection Genetic programming Polynomial regression Tool wear
ISSN:	0264-4401, 1758-7077
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Abstract	Purpose The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear. Design/methodology/approach In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management. Findings In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise. Research limitations/implications The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models. Originality/value In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.
AbstractList	PurposeThe purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.Design/methodology/approachIn this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management.FindingsIn result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise.Research limitations/implicationsThe regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models.Originality/valueIn this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided. Purpose The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear. Design/methodology/approach In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management. Findings In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise. Research limitations/implications The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models. Originality/value In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.
Author	Chen, Jin Dong, Guangming Wang, Hao
Author_xml	– sequence: 1 givenname: Hao surname: Wang fullname: Wang, Hao email: 2018wanghao@sjtu.edu.cn – sequence: 2 givenname: Guangming surname: Dong fullname: Dong, Guangming email: gmdong@sjtu.edu.cn – sequence: 3 givenname: Jin surname: Chen fullname: Chen, Jin email: jinchen@sjtu.edu.cn
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Keywords	Support vector regression Identification Detection Genetic programming Polynomial regression Tool wear
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SubjectTerms	Acoustic emission Algorithms Fault diagnosis Genetic algorithms Machine learning Mutation Neural networks Polynomials Population Regression models Signal processing Support vector machines Tool wear
Title	Application of genetic programming in the identification of tool wear
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