Data-driven Analysis of Product State Propagation in Manufacturing Systems Using Visual Analytics and Machine Learning
The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is...
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| Vydáno v: | Procedia CIRP Ročník 93; s. 449 - 454 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
2020
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| ISSN: | 2212-8271, 2212-8271 |
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| Abstract | The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed. |
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| AbstractList | The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed. |
| Author | Gellrich, Sebastian Filz, Marc-André Herrmann, Christoph Thiede, Sebastian |
| Author_xml | – sequence: 1 givenname: Marc-André surname: Filz fullname: Filz, Marc-André email: m.filz@tu-braunschweig.de organization: Institute of Machine Tools and Production Technology, Chair of Sustainable Manufacturing and Life Cycle Engineering, Technische Universität Braunschweig, Langer Kamp 19 b, 38106 Braunschweig, Germany – sequence: 2 givenname: Sebastian surname: Gellrich fullname: Gellrich, Sebastian organization: Institute of Machine Tools and Production Technology, Chair of Sustainable Manufacturing and Life Cycle Engineering, Technische Universität Braunschweig, Langer Kamp 19 b, 38106 Braunschweig, Germany – sequence: 3 givenname: Christoph surname: Herrmann fullname: Herrmann, Christoph organization: Institute of Machine Tools and Production Technology, Chair of Sustainable Manufacturing and Life Cycle Engineering, Technische Universität Braunschweig, Langer Kamp 19 b, 38106 Braunschweig, Germany – sequence: 4 givenname: Sebastian surname: Thiede fullname: Thiede, Sebastian organization: Institute of Machine Tools and Production Technology, Chair of Sustainable Manufacturing and Life Cycle Engineering, Technische Universität Braunschweig, Langer Kamp 19 b, 38106 Braunschweig, Germany |
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| Cites_doi | 10.1016/j.procs.2011.12.035 10.1016/j.jbusres.2011.01.015 10.1016/j.procir.2017.11.124 10.1007/s10845-013-0761-y 10.1002/mawe.201100908 10.1007/978-3-642-40361-3_1 10.1109/MCG.2007.126 10.1016/0377-0427(87)90125-7 10.1016/j.proeng.2011.04.367 10.1109/TEPM.2010.2055873 10.1109/AIM.2008.4601660 |
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| Keywords | Visual Analytics Intermediate Product States Product Propagation Product State Classes Machine Learning Manufacturing Systems |
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| References | Gebauer, Gustafsson, Witell (bib0001) 2011; 64 Thiede (bib0003) 2018; 69 Cleve, Lämmel (bib00015) 2016 Rousseeuw (bib00016) 1987; 20 Wu, H., Zhang, X., Kuang, Y., Lu, S., 2008. A real-time machine vision system for solder paste inspection, in: The 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. AIM 2008: July 2-5, 2008, Xi’an, China. 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Xian, China. 7/2/2008 - 7/5/2008. IEEE Xplore, [Piscataway, N.J.], pp. 205–210. Wuest, Klein, Seifert, Thoben (bib0007) 2012; 43 Keim, Mansmann, Schneidewind, Thomas, Ziegler (bib00011) 2008 Thomas (bib0008) 2005 Wuest, Klein, Thoben (bib0004) 2011; 10 Wuest, T., Irgens, C., Thoben, K.-D., 2013. Analysis of Manufacturing Process Sequences, Using Machine Learning on Intermediate Product States (as Process Proxy Data), in: Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services. IFIP WG 5. 7 International Conference, APMS 2012, Rhodes, Greece, September 24-26, 2012, Revised Selected Papers, Part II, Berlin, Heidelberg. 2013. Springer Berlin Heidelberg, Berlin/Heidelberg, pp. 1–8. Kohlhammer, Keim, Pohl, Santucci, Andrienko (bib0009) 2011; 7 Cook, Earnshaw, Stasko (bib00010) 2007; 27 Kang, Lee, Choi, Kim, Park, Son, Kim, Noh (bib0002) 2016; 3 Kotu, Deshpande (bib00014) 2014 Huang (bib00012) 2010; 33 Wuest, Irgens, Thoben (bib0005) 2014; 25 Wuest (10.1016/j.procir.2020.03.065_bib0004) 2011; 10 Kohlhammer (10.1016/j.procir.2020.03.065_bib0009) 2011; 7 Kang (10.1016/j.procir.2020.03.065_bib0002) 2016; 3 10.1016/j.procir.2020.03.065_bib0006 Cook (10.1016/j.procir.2020.03.065_bib00010) 2007; 27 10.1016/j.procir.2020.03.065_bib00013 Rousseeuw (10.1016/j.procir.2020.03.065_bib00016) 1987; 20 Kotu (10.1016/j.procir.2020.03.065_bib00014) 2014 Thiede (10.1016/j.procir.2020.03.065_bib0003) 2018; 69 Cleve (10.1016/j.procir.2020.03.065_bib00015) 2016 Wuest (10.1016/j.procir.2020.03.065_bib0005) 2014; 25 Gebauer (10.1016/j.procir.2020.03.065_bib0001) 2011; 64 Wuest (10.1016/j.procir.2020.03.065_bib0007) 2012; 43 Thomas (10.1016/j.procir.2020.03.065_bib0008) 2005 Huang (10.1016/j.procir.2020.03.065_bib00012) 2010; 33 Keim (10.1016/j.procir.2020.03.065_bib00011) 2008 |
| References_xml | – volume: 10 start-page: 2220 year: 2011 end-page: 2225 ident: bib0004 article-title: State of steel products in industrial production processes publication-title: Procedia Engineering – start-page: 446 year: 2014 ident: bib00014 publication-title: Predictive Analytics and Data Mining – start-page: 328 year: 2016 ident: bib00015 publication-title: Data Mining – volume: 43 start-page: 186 year: 2012 end-page: 191 ident: bib0007 article-title: Method to describe interdependencies of state characteristics related to distortion publication-title: Mat.-wiss. u. Werkstofftech. – volume: 69 start-page: 644 year: 2018 end-page: 649 ident: bib0003 article-title: Environmental Sustainability of Cyber Physical Production Systems publication-title: Procedia CIRP – start-page: 186 year: 2005 ident: bib0008 publication-title: Illuminating the path: The research and development agenda for visual analytics – volume: 7 start-page: 117 year: 2011 end-page: 120 ident: bib0009 article-title: Solving Problems with Visual Analytics publication-title: Procedia Computer Science – volume: 33 start-page: 265 year: 2010 end-page: 274 ident: bib00012 article-title: Reducing Solder Paste Inspection in Surface-Mount Assembly Through Mahalanobis–Taguchi Analysis publication-title: IEEE Trans. Electron. Packag. Manufact. – volume: 25 start-page: 1167 year: 2014 end-page: 1180 ident: bib0005 article-title: An approach to monitoring quality in manufacturing using supervised machine learning on product state data publication-title: J Intell Manuf – reference: Wuest, T., Irgens, C., Thoben, K.-D., 2013. Analysis of Manufacturing Process Sequences, Using Machine Learning on Intermediate Product States (as Process Proxy Data), in: Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services. IFIP WG 5. 7 International Conference, APMS 2012, Rhodes, Greece, September 24-26, 2012, Revised Selected Papers, Part II, Berlin, Heidelberg. 2013. Springer Berlin Heidelberg, Berlin/Heidelberg, pp. 1–8. – volume: 3 start-page: 111 year: 2016 end-page: 128 ident: bib0002 article-title: Smart manufacturing: Past research publication-title: Present findings, and future directions. Int. J. of Precis. Eng. and Manuf.-Green Tech. – volume: 27 start-page: 15 year: 2007 end-page: 19 ident: bib00010 article-title: Discovering the unexpected publication-title: IEEE computer graphics and applications – start-page: 76 year: 2008 end-page: 90 ident: bib00011 article-title: Visual Analytics: Scope and Challenges publication-title: Visual data mining. Theory, techniques and tools for visual analytics, vol. 4404 – volume: 64 start-page: 1270 year: 2011 end-page: 1280 ident: bib0001 article-title: Competitive advantage through service differentiation by manufacturing companies publication-title: Journal of Business Research – volume: 20 start-page: 53 year: 1987 end-page: 65 ident: bib00016 article-title: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis publication-title: Journal of Computational and Applied Mathematics – reference: Wu, H., Zhang, X., Kuang, Y., Lu, S., 2008. A real-time machine vision system for solder paste inspection, in: The 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. AIM 2008: July 2-5, 2008, Xi’an, China. 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Xian, China. 7/2/2008 - 7/5/2008. IEEE Xplore, [Piscataway, N.J.], pp. 205–210. – volume: 7 start-page: 117 year: 2011 ident: 10.1016/j.procir.2020.03.065_bib0009 article-title: Solving Problems with Visual Analytics publication-title: Procedia Computer Science doi: 10.1016/j.procs.2011.12.035 – volume: 3 start-page: 111 issue: 1 year: 2016 ident: 10.1016/j.procir.2020.03.065_bib0002 article-title: Smart manufacturing: Past research publication-title: Present findings, and future directions. Int. J. of Precis. Eng. and Manuf.-Green Tech. – volume: 64 start-page: 1270 issue: 12 year: 2011 ident: 10.1016/j.procir.2020.03.065_bib0001 article-title: Competitive advantage through service differentiation by manufacturing companies publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2011.01.015 – start-page: 446 year: 2014 ident: 10.1016/j.procir.2020.03.065_bib00014 – volume: 69 start-page: 644 year: 2018 ident: 10.1016/j.procir.2020.03.065_bib0003 article-title: Environmental Sustainability of Cyber Physical Production Systems publication-title: Procedia CIRP doi: 10.1016/j.procir.2017.11.124 – start-page: 328 year: 2016 ident: 10.1016/j.procir.2020.03.065_bib00015 – start-page: 186 year: 2005 ident: 10.1016/j.procir.2020.03.065_bib0008 – volume: 25 start-page: 1167 issue: 5 year: 2014 ident: 10.1016/j.procir.2020.03.065_bib0005 article-title: An approach to monitoring quality in manufacturing using supervised machine learning on product state data publication-title: J Intell Manuf doi: 10.1007/s10845-013-0761-y – volume: 43 start-page: 186 issue: 1-2 year: 2012 ident: 10.1016/j.procir.2020.03.065_bib0007 article-title: Method to describe interdependencies of state characteristics related to distortion publication-title: Mat.-wiss. u. Werkstofftech. doi: 10.1002/mawe.201100908 – ident: 10.1016/j.procir.2020.03.065_bib0006 doi: 10.1007/978-3-642-40361-3_1 – volume: 27 start-page: 15 issue: 5 year: 2007 ident: 10.1016/j.procir.2020.03.065_bib00010 article-title: Discovering the unexpected publication-title: IEEE computer graphics and applications doi: 10.1109/MCG.2007.126 – start-page: 76 year: 2008 ident: 10.1016/j.procir.2020.03.065_bib00011 article-title: Visual Analytics: Scope and Challenges – volume: 20 start-page: 53 year: 1987 ident: 10.1016/j.procir.2020.03.065_bib00016 article-title: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis publication-title: Journal of Computational and Applied Mathematics doi: 10.1016/0377-0427(87)90125-7 – volume: 10 start-page: 2220 year: 2011 ident: 10.1016/j.procir.2020.03.065_bib0004 article-title: State of steel products in industrial production processes publication-title: Procedia Engineering doi: 10.1016/j.proeng.2011.04.367 – volume: 33 start-page: 265 issue: 4 year: 2010 ident: 10.1016/j.procir.2020.03.065_bib00012 article-title: Reducing Solder Paste Inspection in Surface-Mount Assembly Through Mahalanobis–Taguchi Analysis publication-title: IEEE Trans. Electron. Packag. Manufact. doi: 10.1109/TEPM.2010.2055873 – ident: 10.1016/j.procir.2020.03.065_bib00013 doi: 10.1109/AIM.2008.4601660 |
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| SubjectTerms | Intermediate Product States Machine Learning Manufacturing Systems Product Propagation Product State Classes Visual Analytics |
| Title | Data-driven Analysis of Product State Propagation in Manufacturing Systems Using Visual Analytics and Machine Learning |
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