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An approach to monitoring quality in manufacturing using supervised machine learning on product state data

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Abstract

Increasing market demand towards higher product and process quality and efficiency forces companies to think of new and innovative ways to optimize their production. In the area of high-tech manufacturing products, even slight variations of the product state during production can lead to costly and time-consuming rework or even scrapage. Describing an individual product’s state along the entire manufacturing programme, including all relevant information involved for utilization, e.g., in-process adjustments of process parameters, can be one way to meet the quality requirements and stay competitive. Ideally, the gathered information can be directly analyzed and in case of an identified critical trend or event, adequate action, such as an alarm, can be triggered. Traditional methods based on modelling of cause-effect relations reaches its limits due to the fast increasing complexity and high-dimensionality of modern manufacturing programmes. There is a need for new approaches that are able to cope with this complexity and high-dimensionality which, at the same time, are able to generate applicable results with reasonable effort. Within this paper, the possibility to generate such a system by applying a combination of Cluster Analysis and Supervised Machine Learning on product state data along the manufacturing programme will be presented. After elaborating on the different key aspects of the approach, the applicability on the identified problem in industrial environment will be discussed briefly.

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References

  • Albino, V., Pontrandolfo, P., & Scozzi, B. (2002). Analysis of information flows to enhance the coordination of production processes. International Journal of Production Economics, 75, 7–19.

    Article  Google Scholar 

  • Apley, D., & Shi, J. (2001). A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes. Technometrics, 43(1), 84–95.

    Article  Google Scholar 

  • Babiceanu, R., & Chen, F. (2006). Development and applications of holonic manufacturing systems: A survey. Journal of Intelligent Manufacturing, 17(1), 111–131.

    Article  Google Scholar 

  • Borror, C., Montgomery, D., & Runger, G. (1999). Robustness of the EWMA control chart to non-normality. Journal of Quality Technology, 31(3), 309–316.

    Google Scholar 

  • Boon-itt, S. (2010). An empirical model of the relationship between manufacturing capabilities: Evidence from the Thai automotive industry. NIDA Development Journal, 59(2), 19–45.

    Google Scholar 

  • Brinksmeier, E. (1991). Prozeß- und Werkstückqualität in der Feinbearbeitung. Fortschritt-Berichte VDI Reihe2: Fertigungstechnik Nr. 234. Düsseldorf: VDI Verlag.

  • Chinnam, R. B. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40(17), 4449–4466.

    Article  Google Scholar 

  • Chou, Y., Polansky, A., & Mason, R. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133–141.

    Google Scholar 

  • Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.

    Article  Google Scholar 

  • Clausen, B., Frerichs, F., Goch, G., Klein, D., Lübben, Th, Nowag, L., et al. (2006). Verzugsentstehung von Wälzlagerringen—Eine prozesskettenübergreifende Analyse. HTM Z. Werkst. Wärmebeh. Fertigung, 6(61), 309–319.

  • de Groot, P. J., Postma, G. J., Melssen, W. J., & Buydens, L. M. C. (1999). Selecting a representative training set for the classification of demolition waste using remote NIR sensing. Analytica Chimica Acta, 392(1999), 67–75.

    Article  Google Scholar 

  • Ding, Y., Ceglarek, D., & Shi, J. (2002). Fault diagnosis of multistage manufacturing processes by using state space approach. Journal of Manufacturing Science and Engineering, 124(2), 313–322. doi:10.1115/1.1445155.

    Google Scholar 

  • Du, R., Elbestawi, M. A., & Wu, S. M. (1995). Automated monitoring of manufacturing processes, part 1: Monitoring methods. Journal of Engineering for Industry, 117(2), 121–132.

    Article  Google Scholar 

  • Fischer, D., & Breitenbach, J. (Eds.). (2009). Die Pharmaindustrie: Einblick, Durchblick, Perspektiven. Heidelberg: Spektrum Akademischer Verlag.

    Google Scholar 

  • Giebel, M., Essmann, H., Du Preez, N., & Jochem, R. (2009). Improved innovation through the integration of quality gates into the enterprise and product lifecycle roadmaps. CIRP Journal of Manufacturing Science and Technology, 1(3), 199–205.

    Article  Google Scholar 

  • Gogouvitis, S., Konstanteli, K., Waldschmidt, S., Kousiouris, G., Katsaros, G., Menychtas, A., et al. (2012). Workflow management for soft real-time interactive applications in virtualized environments. Future Generation Computer Systems, 28(1), 193–209.

    Article  Google Scholar 

  • Hamel, L. (2009). Knowledge discovery with support vector machines. Hoboken: Wiley, ISBN 978-0-470-37192-3.

  • Hicks, B. J., Culley, S. J., & McMohan, C. A. (2006). A study of issues relating to information management across engineering SMEs. International Journal of Information Management, 26, 267–289.

    Article  Google Scholar 

  • Holcomb, M. C. (1994). Customer service measurement: A methodology for increasing customer value through utilization of the Taguchi strategy. Journal of Business Logistics, 15(1), 29–52.

    Google Scholar 

  • Jacob, J., & Petrick, K. (2007). Qualitätsmanagement und Normung. In R. Schmitt & T. Pfeifer (Eds.), Masing Handbuch Qualitätsmanagement (pp. 101–121). München: Carl Hanser Verlag.

    Google Scholar 

  • Jansen-Vullers, M. H., van Drop, C. A., & Beulens, A. J. M. (2003). Managing traceability information in manufacture. International Journal of Information Management, 23, 395–413.

    Article  Google Scholar 

  • Jenab, K., & Ahi, P. (2010). Fuzzy quality feature monitoring model. International Journal of Production Research, 48(17), 5021–5030.

    Article  Google Scholar 

  • Jiang, P., Jia, F., Wang, Y., & Zheng, M. (2012). Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. Journal of Intelligent Manufacturing (online first-2012).

  • Kalpakjian, S., & Schmid, S. R. (2009). Manufacturing engineering and technology. New Jersey: Prentice Hall.

    Google Scholar 

  • Kessler, O., Prinz, Ch., Sackmann, T., Nowag, L., Surm, H., Frerichs, F., et al. (2006). Experimental study of distortion phenomena in manufacturing lines. Materialwissenschaft und Werkstofftechnik, 37(1), 11–18.

    Article  Google Scholar 

  • Koufteros, X. A., Vonderembse, M. A., & Doll, W. J. (2002). Examine the competitive capabilities of manufacturing firms. Structural Equation Modelling, 9(2), 256–282.

    Article  Google Scholar 

  • Kovacic, M., & Sarler, B. (2009). Application of the genetic programming for increasing the soft annealing productivity in steel industry. Materials and Manufacturing Processes, 24, 369–374.

    Article  Google Scholar 

  • Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Megahed, F. M., & Camelio, J. A. (2010). Real-time fault detection in manufacturing environments using face recognition techniques. Journal of Intelligent Manufacturing, 23(3), 393–408.

    Article  Google Scholar 

  • Merali, Y., & Bennet, Z. (2011). Web 2.0 and Network Intelligence. In P. Warren, J. Davies, & E. Simperl (Eds.), Context and semantics for knowledge management (pp. 11–26). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Mizuyama, H. (2004). Directing quality improvement efforts in a multi-stage production process through observational data analysis. In Proceedings of the 8th international conference on manufacturing and management, pp. 414–421.

  • Mizuyama, H. (2006). it Artificial-neural-network-based MSQIM for exploratory analysis of manufacturing data. In Proceedings of the 7th Asia-Pacific industrial engineering and management systems conference.

  • Mohanty, P. P. (2004). An agent-oriented approach to resolve the production planning complexities for a modern steel manufacturing system. International Journal of Advanced Manufacturing Technology, 24, 199–205.

    Article  Google Scholar 

  • Monostori, L., Váncza, J., & Kumara, S. R. T. (2006). Agent-based systems for manufacturing. CIRP Annals-Manufacturing Technology, 55(2), 697–720.

    Article  Google Scholar 

  • Pavletic, D., & Sokovic, M. (2009). Quality improvement model at the manufacturing process preparation level. International Journal of Quality Research, 3(4), 309–315.

    Google Scholar 

  • Robinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96(3), 315–337.

    Article  Google Scholar 

  • Scheidat, T., Leich, M., Alexander, M., & Vielhauer, C. (2009). Support vector machines for dynamic biometric handwriting classification. In Proceedings of AIAI Workshops, pp. 118–125.

  • Seifert, M. (2009). Collaboration formation in virtual organisations by applying prospective performance measurement. Bremer Schriften zur Integrierten Produkt- und Prozessentwicklung: Dissertation at the University of Bremen.

  • Sha, L., Abdelzaher, T., Arzen, K.-E., Cervin, A., Baker, T., Burns, A., et al. (2004). Real-time scheduling theory: A historical perspective. Real-Time Systems, 28(2–3), 101–155.

    Article  Google Scholar 

  • Shukla, C. S., & Frank Chen, F. (1996). The state of the art in intelligent real-time FMS control: A comprehensive survey. Journal of Intelligent Manufacturing, 7(6), 441–455.

    Article  Google Scholar 

  • Silva, R. G. (2009). Condition monitoring of the cutting process using a self-organizing spiking neural network map. Journal of Intelligent Manufacturing, 21(6), 823–829.

    Google Scholar 

  • Siyasiya, C., van Rooyen, G. T., & Stumpf, W. E. (2005). Metallurgical factors that affect the strand width during continuous casting of DIN 1.4003 stainless steel. The Journal of The South African Institute of Mining and Metallurgy, 105, 473–481.

    Google Scholar 

  • Spath, D., Scharer, M., Landwehr, R., Förster, H., & Schneider, W. (2001). Tore öffnen—Quality-Gate-Konzept für den Produktentstehungsprozess. QZ Qualität und Zuverlässigkeit, 46(12), 1544–1549.

    Google Scholar 

  • Stoumbos, Z., & Sullivan, J. (2002). Robustness to non-normality of the multivariate EWMA control chart. Journal of Quality Technology, 34(3), 260–276.

    Google Scholar 

  • Sukchotrat, T., Kim, S. B., & Tsung, F. (2009). One-class classification-based control charts for multivariate process monitoring. IIE Transactions, 42(2), 107–120.

    Article  Google Scholar 

  • Vapnik, V. (1998). Statistical learning theory. Hoboken: Wiley.

    Google Scholar 

  • Wang, K., & Tsung, F. (2007). Run-to-run process adjustment using categorical observations. Journal of Quality Technology, 39(4), 312–325.

    Google Scholar 

  • Wuest, T., Irgens, C. & Thoben, K.-D. (2012). Analysis of manufacturing process sequences, using machine learning on intermediate product states (as process proxy data). In C. Emmanouilidis, M. Taisch, & D. Kiritsis (Eds.), Competitive manufacturing for innovative products and services. In Proceedings of the APMS 2012 international conference of advances in production management systems, September 24–26, 2012, Rhodes Island, Greece. (to be published by Springer).

  • Wuest, T., Klein, D., & Thoben, K.-D. (2011). State of steel products in industrial production processes. Procedia Engineering, 10, 2220–2225.

    Article  Google Scholar 

  • Yu, T. & Wang, G. (2009). The process quality control of single-piece and small-batch products in advanced manufacturing environment. In Proceedings of the 16th international conference on industrial engineering and engineering management. (IE &EM ’09), October 21–23, 2009, Beijing, China, pp. 306–310.

  • Zantek, P. F., Wright, G. P., & Plante, R. D. (2006). A self-starting procedure for monitoring process quality in multistage manufacturing systems. IIE Transactions, 38(4), 293–308.

    Article  Google Scholar 

  • Zhang, J., & Wang, H. (2009). A minimized zero mean entropy approach to networked control systems. In Proceedings of the 48th IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference (pp. 6876–6881). Shanghai, China: IEEE. doi:10.1109/CDC.2009.5400679.

  • Zoch, H.-W. & Lübben, Th. (2011). Verzugsbeherrschung—Systemorientierter Ansatz als wesentliche Voraussetzung für den Erfolg. Tagungsband zum 26. Aachener Stahlkolloquium, Verlagshaus Mainz.

  • Zoch, H.-W. (2012). Distortion engineering-interim results after one decade research within the Collaborative Research Center. Materialwissenschaft und Werkstofftechnik, 43(1–2), 9–15.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the “Deutsche Forschungsgemeinschaft” for financial support via the funded project “Informationssystem für werkstoffwissenschaftliche Forschungsdaten”.

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Authors and Affiliations

  1. Applied Information and Communication Technology for Production, BIBA – Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20, 28359 , Bremen, Germany

    Thorsten Wuest & Klaus-Dieter Thoben

  2. Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow, G1 1XQ, UK

    Christopher Irgens

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  1. Thorsten Wuest
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Correspondence to Thorsten Wuest.

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Wuest, T., Irgens, C. & Thoben, KD. An approach to monitoring quality in manufacturing using supervised machine learning on product state data. J Intell Manuf 25, 1167–1180 (2014). https://doi.org/10.1007/s10845-013-0761-y

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