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

Journal of Intelligent Manufacturing

Volumn 25, Issue 5, 2014, Pages 1167-1180

  Wuest, Thorsten ^a   Irgens, Christopher ^b   Thoben, Klaus Dieter ^a  

^a UNIVERSITY OF BREMEN   (Germany)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Cluster analysis; Data; Manufacturing; Process; Product state; Quality monitoring; Supervised machine learning

Indexed keywords

CLUSTER ANALYSIS; PROCESSING; SUPERVISED LEARNING;

DATA; HIGH DIMENSIONALITY; INDUSTRIAL ENVIRONMENTS; MANUFACTURING PRODUCTS; PRODUCT STATE; QUALITY MONITORING; QUALITY REQUIREMENTS; SUPERVISED MACHINE LEARNING;

MANUFACTURE;

EID: 84906948333     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-013-0761-y     Document Type: Conference Paper

References (54)

1. 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.
2. Apley, D., & Shi, J. (2001). A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes. Technometrics, 43(1), 84-95.
3. Babiceanu, R., & Chen, F. (2006). Development and applications of holonic manufacturing systems: A survey. Journal of Intelligent Manufacturing, 17(1), 111-131.
4. Borror, C., Montgomery, D., & Runger, G. (1999). Robustness of the EWMA control chart to non-normality. Journal of Quality Technology, 31(3), 309-316.
5. 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.
6. Brinksmeier, E. (1991). Prozeß- und Werkstückqualität in der Feinbearbeitung. Fortschritt-Berichte VDI Reihe2: Fertigungstechnik Nr. 234. Düsseldorf: VDI Verlag.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Fischer, D., & Breitenbach, J. (Eds.). (2009). Die Pharmaindustrie: Einblick, Durchblick, Perspektiven. Heidelberg: Spektrum Akademischer Verlag.
15. 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.
16. 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.
17. Hamel, L. (2009). Knowledge discovery with support vector machines. Hoboken: Wiley, ISBN 978-0-470-37192-3.
18. 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.
19. 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.
20. Jacob, J., & Petrick, K. (2007). Qualitätsmanagement und Normung. In R. Schmitt & T. Pfeifer (Eds.), Masing Handbuch Qualitätsmanagement (pp. 101-121). München: Carl Hanser Verlag.
21. 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.
22. Jenab, K., & Ahi, P. (2010). Fuzzy quality feature monitoring model. International Journal of Production Research, 48(17), 5021-5030.
23. 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).
24. Kalpakjian, S., & Schmid, S. R. (2009). Manufacturing engineering and technology. New Jersey: Prentice Hall.
25. 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.
26. Koufteros, X. A., Vonderembse, M. A., & Doll, W. J. (2002). Examine the competitive capabilities of manufacturing firms. Structural Equation Modelling, 9(2), 256-282.
27. 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.
28. Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval. Cambridge, UK: Cambridge University Press.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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.
34. Monostori, L., Váncza, J., & Kumara, S. R. T. (2006). Agent-based systems for manufacturing. CIRP Annals-Manufacturing Technology, 55(2), 697-720.
35. Pavletic, D., & Sokovic, M. (2009). Quality improvement model at the manufacturing process preparation level. International Journal of Quality Research, 3(4), 309-315.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. Stoumbos, Z., & Sullivan, J. (2002). Robustness to non-normality of the multivariate EWMA control chart. Journal of Quality Technology, 34(3), 260-276.
45. Sukchotrat, T., Kim, S. B., & Tsung, F. (2009). One-class classification-based control charts for multivariate process monitoring. IIE Transactions, 42(2), 107-120.
46. Vapnik, V. (1998). Statistical learning theory. Hoboken: Wiley.
47. Wang, K., & Tsung, F. (2007). Run-to-run process adjustment using categorical observations. Journal of Quality Technology, 39(4), 312-325.
48. 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).
49. Wuest, T., Klein, D., & Thoben, K.-D. (2011). State of steel products in industrial production processes. Procedia Engineering, 10, 2220-2225.
50. 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.
51. 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.
52. 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.
53. Zoch, H.-W. & Lübben, Th. (2011). Verzugsbeherrschung - Systemorientierter Ansatz als wesentliche Voraussetzung für den Erfolg. Tagungsband zum 26. Aachener Stahlkolloquium, Verlagshaus Mainz.
54. 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.