An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the qua...
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Gespeichert in:

Autor*in:	Sarmad Hameed [verfasserIn] Faraz Junejo [verfasserIn] Imran Amin [verfasserIn] Asif Khalid Qureshi [verfasserIn] Irfan Khan Tanoli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	artificial neural network (ANN) gear hobbing Industry 4.0 remaining useful life (RUL) tool condition monitoring (TCM) artificial intelligence

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 16(2023), 17, p 6143
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:17, p 6143

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en16176143

Katalog-ID:	DOAJ093520816

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allfields	10.3390/en16176143 doi (DE-627)DOAJ093520816 (DE-599)DOAJbd80f1831d224e438e94ce8bf527bbc7 DE-627 ger DE-627 rakwb eng Sarmad Hameed verfasserin aut An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing. artificial neural network (ANN) gear hobbing Industry 4.0 remaining useful life (RUL) tool condition monitoring (TCM) artificial intelligence Technology T Faraz Junejo verfasserin aut Imran Amin verfasserin aut Asif Khalid Qureshi verfasserin aut Irfan Khan Tanoli verfasserin aut In Energies MDPI AG, 2008 16(2023), 17, p 6143 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:17, p 6143 https://doi.org/10.3390/en16176143 kostenfrei https://doaj.org/article/bd80f1831d224e438e94ce8bf527bbc7 kostenfrei https://www.mdpi.com/1996-1073/16/17/6143 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 17, p 6143
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allfieldsSound	10.3390/en16176143 doi (DE-627)DOAJ093520816 (DE-599)DOAJbd80f1831d224e438e94ce8bf527bbc7 DE-627 ger DE-627 rakwb eng Sarmad Hameed verfasserin aut An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing. artificial neural network (ANN) gear hobbing Industry 4.0 remaining useful life (RUL) tool condition monitoring (TCM) artificial intelligence Technology T Faraz Junejo verfasserin aut Imran Amin verfasserin aut Asif Khalid Qureshi verfasserin aut Irfan Khan Tanoli verfasserin aut In Energies MDPI AG, 2008 16(2023), 17, p 6143 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:17, p 6143 https://doi.org/10.3390/en16176143 kostenfrei https://doaj.org/article/bd80f1831d224e438e94ce8bf527bbc7 kostenfrei https://www.mdpi.com/1996-1073/16/17/6143 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 17, p 6143
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author	Sarmad Hameed
spellingShingle	Sarmad Hameed misc artificial neural network (ANN) misc gear hobbing misc Industry 4.0 misc remaining useful life (RUL) misc tool condition monitoring (TCM) misc artificial intelligence misc Technology misc T An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data
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topic_title	An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data artificial neural network (ANN) gear hobbing Industry 4.0 remaining useful life (RUL) tool condition monitoring (TCM) artificial intelligence
topic	misc artificial neural network (ANN) misc gear hobbing misc Industry 4.0 misc remaining useful life (RUL) misc tool condition monitoring (TCM) misc artificial intelligence misc Technology misc T
topic_unstemmed	misc artificial neural network (ANN) misc gear hobbing misc Industry 4.0 misc remaining useful life (RUL) misc tool condition monitoring (TCM) misc artificial intelligence misc Technology misc T
topic_browse	misc artificial neural network (ANN) misc gear hobbing misc Industry 4.0 misc remaining useful life (RUL) misc tool condition monitoring (TCM) misc artificial intelligence misc Technology misc T
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title	An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data
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title_full	An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data
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title_sort	intelligent deep learning technique for predicting hobbing tool wear based on gear hobbing using real-time monitoring data
title_auth	An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data
abstract	Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.
abstractGer	Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.
abstract_unstemmed	Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.
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title_short	An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data
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up_date	2024-07-03T17:48:51.272Z
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An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

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