Research on tool wear prediction based on temperature signals and deep learning

Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent c...
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Gespeichert in:

Autor*in:	He, Zhaopeng [verfasserIn] Shi, Tielin [verfasserIn] Xuan, Jianping [verfasserIn] Li, Tianxiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning

Übergeordnetes Werk:	Enthalten in: Wear - Amsterdam [u.a.] : Elsevier Science, 1957, 478
Übergeordnetes Werk:	volume:478

DOI / URN:	10.1016/j.wear.2021.203902

Katalog-ID:	ELV006061494

Internformat


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520			\|a Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed.
650		4	\|a Tool wear
650		4	\|a Deep learning
650		4	\|a Stacked sparse autoencoders
650		4	\|a Cutting temperature
650		4	\|a Machine learning
650		4	\|a Turning
700	1		\|a Shi, Tielin \|e verfasserin \|0 (orcid)0000-0001-6977-9700 \|4 aut
700	1		\|a Xuan, Jianping \|e verfasserin \|4 aut
700	1		\|a Li, Tianxiang \|e verfasserin \|4 aut
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allfields	10.1016/j.wear.2021.203902 doi (DE-627)ELV006061494 (ELSEVIER)S0043-1648(21)00291-X DE-627 ger DE-627 rda eng 670 DE-600 52.12 bkl He, Zhaopeng verfasserin aut Research on tool wear prediction based on temperature signals and deep learning 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed. Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning Shi, Tielin verfasserin (orcid)0000-0001-6977-9700 aut Xuan, Jianping verfasserin aut Li, Tianxiang verfasserin aut Enthalten in Wear Amsterdam [u.a.] : Elsevier Science, 1957 478 Online-Ressource (DE-627)306714027 (DE-600)1501123-9 (DE-576)098474030 0043-1648 nnns volume:478 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 478
spelling	10.1016/j.wear.2021.203902 doi (DE-627)ELV006061494 (ELSEVIER)S0043-1648(21)00291-X DE-627 ger DE-627 rda eng 670 DE-600 52.12 bkl He, Zhaopeng verfasserin aut Research on tool wear prediction based on temperature signals and deep learning 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed. Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning Shi, Tielin verfasserin (orcid)0000-0001-6977-9700 aut Xuan, Jianping verfasserin aut Li, Tianxiang verfasserin aut Enthalten in Wear Amsterdam [u.a.] : Elsevier Science, 1957 478 Online-Ressource (DE-627)306714027 (DE-600)1501123-9 (DE-576)098474030 0043-1648 nnns volume:478 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 478
allfields_unstemmed	10.1016/j.wear.2021.203902 doi (DE-627)ELV006061494 (ELSEVIER)S0043-1648(21)00291-X DE-627 ger DE-627 rda eng 670 DE-600 52.12 bkl He, Zhaopeng verfasserin aut Research on tool wear prediction based on temperature signals and deep learning 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed. Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning Shi, Tielin verfasserin (orcid)0000-0001-6977-9700 aut Xuan, Jianping verfasserin aut Li, Tianxiang verfasserin aut Enthalten in Wear Amsterdam [u.a.] : Elsevier Science, 1957 478 Online-Ressource (DE-627)306714027 (DE-600)1501123-9 (DE-576)098474030 0043-1648 nnns volume:478 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 478
allfieldsGer	10.1016/j.wear.2021.203902 doi (DE-627)ELV006061494 (ELSEVIER)S0043-1648(21)00291-X DE-627 ger DE-627 rda eng 670 DE-600 52.12 bkl He, Zhaopeng verfasserin aut Research on tool wear prediction based on temperature signals and deep learning 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed. Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning Shi, Tielin verfasserin (orcid)0000-0001-6977-9700 aut Xuan, Jianping verfasserin aut Li, Tianxiang verfasserin aut Enthalten in Wear Amsterdam [u.a.] : Elsevier Science, 1957 478 Online-Ressource (DE-627)306714027 (DE-600)1501123-9 (DE-576)098474030 0043-1648 nnns volume:478 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 478
allfieldsSound	10.1016/j.wear.2021.203902 doi (DE-627)ELV006061494 (ELSEVIER)S0043-1648(21)00291-X DE-627 ger DE-627 rda eng 670 DE-600 52.12 bkl He, Zhaopeng verfasserin aut Research on tool wear prediction based on temperature signals and deep learning 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed. Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning Shi, Tielin verfasserin (orcid)0000-0001-6977-9700 aut Xuan, Jianping verfasserin aut Li, Tianxiang verfasserin aut Enthalten in Wear Amsterdam [u.a.] : Elsevier Science, 1957 478 Online-Ressource (DE-627)306714027 (DE-600)1501123-9 (DE-576)098474030 0043-1648 nnns volume:478 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 478
language	English
source	Enthalten in Wear 478 volume:478
sourceStr	Enthalten in Wear 478 volume:478
format_phy_str_mv	Article
bklname	Tribologie
institution	findex.gbv.de
topic_facet	Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning
dewey-raw	670
isfreeaccess_bool	false
container_title	Wear
authorswithroles_txt_mv	He, Zhaopeng @@aut@@ Shi, Tielin @@aut@@ Xuan, Jianping @@aut@@ Li, Tianxiang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	306714027
dewey-sort	3670
id	ELV006061494
language_de	englisch
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author	He, Zhaopeng
spellingShingle	He, Zhaopeng ddc 670 bkl 52.12 misc Tool wear misc Deep learning misc Stacked sparse autoencoders misc Cutting temperature misc Machine learning misc Turning Research on tool wear prediction based on temperature signals and deep learning
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topic_title	670 DE-600 52.12 bkl Research on tool wear prediction based on temperature signals and deep learning Tool wear Deep learning Stacked sparse autoencoders Cutting temperature Machine learning Turning
topic	ddc 670 bkl 52.12 misc Tool wear misc Deep learning misc Stacked sparse autoencoders misc Cutting temperature misc Machine learning misc Turning
topic_unstemmed	ddc 670 bkl 52.12 misc Tool wear misc Deep learning misc Stacked sparse autoencoders misc Cutting temperature misc Machine learning misc Turning
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title	Research on tool wear prediction based on temperature signals and deep learning
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title_full	Research on tool wear prediction based on temperature signals and deep learning
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title_sort	research on tool wear prediction based on temperature signals and deep learning
title_auth	Research on tool wear prediction based on temperature signals and deep learning
abstract	Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed.
abstractGer	Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed.
abstract_unstemmed	Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed.
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title_short	Research on tool wear prediction based on temperature signals and deep learning
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author2	Shi, Tielin Xuan, Jianping Li, Tianxiang
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doi_str	10.1016/j.wear.2021.203902
up_date	2024-07-06T20:05:30.934Z
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Research on tool wear prediction based on temperature signals and deep learning

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