Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast pa...

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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 66; číslo 5; s. 3794 - 3803
Hlavní autoři:	Shi, Chengming, Panoutsos, George, Luo, Bo, Liu, Hongqi, Li, Bin, Lin, Xu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Classification Computer simulation Deep learning Deep learning (DL) Feature extraction feature fusion feature spaces Hidden Markov models Machine learning Machinery condition monitoring Machining Manufacturing Manufacturing processes tool condition monitoring (TCM) Tool wear ultraprecision manufacturing process
ISSN:	0278-0046, 1557-9948
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Abstract	Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies.
AbstractList	Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies.
Author	Lin, Xu Li, Bin Luo, Bo Liu, Hongqi Shi, Chengming Panoutsos, George
Author_xml	– sequence: 1 givenname: Chengming orcidid: 0000-0002-6530-5695 surname: Shi fullname: Shi, Chengming email: 513864035@qq.com organization: Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: George orcidid: 0000-0002-7395-8418 surname: Panoutsos fullname: Panoutsos, George email: g.panoutsos@sheffield.ac.uk organization: Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, United Kingdom – sequence: 3 givenname: Bo orcidid: 0000-0002-2249-8263 surname: Luo fullname: Luo, Bo email: hglobo@163.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 4 givenname: Hongqi surname: Liu fullname: Liu, Hongqi email: liuhongqi328@163.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Bin orcidid: 0000-0002-8722-8934 surname: Li fullname: Li, Bin email: li_bin_hust@163.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 6 givenname: Xu surname: Lin fullname: Lin, Xu email: m201670426@hust.edu.cn organization: Huazhong University of Science and Technology, Wuhan, China
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Cites_doi	10.1109/MFI.2015.7295820 10.1109/TPAMI.2013.50 10.1109/TEPM.2010.2052811 10.1109/TIM.2016.2584238 10.1109/TIM.2017.2759418 10.1016/j.ymssp.2006.07.016 10.1016/j.jsv.2016.05.027 10.1126/science.1127647 10.1109/TII.2012.2205583 10.1109/TIM.2013.2281576 10.1007/s00170-011-3504-2 10.1007/s00170-016-9735-5 10.1016/j.ymssp.2016.08.035 10.1109/TIE.2017.2733438 10.1109/ICARCV.2010.5707842 10.1016/j.ins.2013.06.010 10.1109/TIE.2017.2745473 10.1016/j.jprocont.2014.01.012 10.1109/JSYST.2015.2425793 10.1016/j.knosys.2016.12.012 10.1016/j.eswa.2010.12.095 10.1016/j.eswa.2013.12.026 10.1145/1553374.1553453 10.1109/TIE.2016.2519325 10.7551/mitpress/7503.003.0147 10.1016/B978-0-08-096532-1.01330-3
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References	ref13 ref15 ref14 ref11 ref10 ref2 patra (ref7) 2011; 3 ref1 jemielniak (ref30) 2012; 59 ref17 ref16 ref19 ref18 vincent (ref28) 2010; 11 ref24 ref23 li (ref3) 2017; 91 ref26 hinton (ref25) 2006; 313 ku (ref8) 2014; 12 ref20 ref22 ref21 qiu (ref12) 2014; 12 ref27 ref29 ref9 ref4 ref6 ref5
References_xml	– ident: ref9 doi: 10.1109/MFI.2015.7295820 – ident: ref19 doi: 10.1109/TPAMI.2013.50 – ident: ref1 doi: 10.1109/TEPM.2010.2052811 – ident: ref14 doi: 10.1109/TIM.2016.2584238 – ident: ref27 doi: 10.1109/TIM.2017.2759418 – ident: ref13 doi: 10.1016/j.ymssp.2006.07.016 – ident: ref23 doi: 10.1016/j.jsv.2016.05.027 – volume: 313 start-page: 504 year: 2006 ident: ref25 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – ident: ref6 doi: 10.1109/TII.2012.2205583 – ident: ref5 doi: 10.1109/TIM.2013.2281576 – volume: 59 start-page: 73 year: 2012 ident: ref30 article-title: Tool condition monitoring based on numerous signal features publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-011-3504-2 – volume: 12 start-page: 40 year: 2014 ident: ref12 article-title: Tool wear monitoring based on wavelet packet coefficient and hidden Markov model publication-title: Machine Tool & Hydraulics – volume: 91 start-page: 351 year: 2017 ident: ref3 article-title: Force-based tool condition monitoring for turning process using v-support vector regression publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-016-9735-5 – ident: ref16 doi: 10.1016/j.ymssp.2016.08.035 – ident: ref15 doi: 10.1109/TIE.2017.2733438 – volume: 3 start-page: 2126 year: 2011 ident: ref7 article-title: Acoustic emission based tool condition monitoring system in drilling publication-title: Lecture Notes Computational Sci Engrg – volume: 11 start-page: 3371 year: 2010 ident: ref28 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – volume: 12 start-page: 68 year: 2014 ident: ref8 article-title: Recognition of tool wear state based on wavelet packet and BP neural network publication-title: Modern Manufature Engineering – ident: ref10 doi: 10.1109/ICARCV.2010.5707842 – ident: ref11 doi: 10.1016/j.ins.2013.06.010 – ident: ref20 doi: 10.1109/TIE.2017.2745473 – ident: ref17 doi: 10.1016/j.jprocont.2014.01.012 – ident: ref2 doi: 10.1109/JSYST.2015.2425793 – ident: ref21 doi: 10.1016/j.knosys.2016.12.012 – ident: ref29 doi: 10.1016/j.eswa.2010.12.095 – ident: ref22 doi: 10.1016/j.eswa.2013.12.026 – ident: ref24 doi: 10.1145/1553374.1553453 – ident: ref18 doi: 10.1109/TIE.2016.2519325 – ident: ref26 doi: 10.7551/mitpress/7503.003.0147 – ident: ref4 doi: 10.1016/B978-0-08-096532-1.01330-3
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SubjectTerms	Classification Computer simulation Deep learning Deep learning (DL) Feature extraction feature fusion feature spaces Hidden Markov models Machine learning Machinery condition monitoring Machining Manufacturing Manufacturing processes tool condition monitoring (TCM) Tool wear ultraprecision manufacturing process
Title	Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing
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