Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

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Abstract:

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

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March 2015

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[1] German Federal Ministry of Education and Research, Industrie 4. 0 – Innovationen für die Produktion von Morgen, (2014).

Google Scholar

[2] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From Data Mining to Knowledge Discovery in Databases, American Association for Artificial Intelligence Press / The MIT Press, Massachusetts Institute Of Technology, AI Magazine 17 (1996) 37-54.

Google Scholar

[3] V. Shrivastava, N. Sharma, Artificial Neural Network Based Optical Character Recognition, in: Signal & Image Processing: An International Journal (SIPIJ) Vol. 3, No. 5, (2012).

DOI: 10.5121/sipij.2012.3506

Google Scholar

[4] A. Y. Ng, A. Coates, M. Diel, V. Ganapathi, J. Schulte, B. Tse, E. Berger, E. Liang, Autonomous inverted helicopter flight via reinforcement learning, in: 9th International symposium on experimental robotics, 2006, pp.363-372.

DOI: 10.1007/11552246_35

Google Scholar

[5] C. Gröger, F. Niedermann, B. Mitschang, Data Mining-driven Manufacturing Process Optimization, in: Proceedings of the World Congress on Engineering 2012 Vol III WCE 2012 Conference, London, (2012).

Google Scholar

[6] M. Große Böckmann, R. Krappig, M. Stolorz, R. Schmitt, Data-Mining in der Produktion, wt Werkstattstechnik online 103 (2013) 921-925.

DOI: 10.37544/1436-4980-2013-11-12-921

Google Scholar

[7] P. Groche, J. Schreiner, J. Hohmann, M. Birkhold, A. Lechler, Industrie 4. 0 – Chance auch für die Umformtechnik, in: VDI-Z, Integrierte Produktion 156 (2014) 28-31.

Google Scholar

[8] L. Cser, T. Menzel, Data Mining in der Qualitätssicherung umformtechnischer Prozesse, Bay Zoltan Stiftung für angewandte Forschung, Budapest, LFT Erlangen, Umformtechnik 2000 PLUS (1999) 101-110.

Google Scholar

[9] R. Mork, Qualitätsbewertung und -regelung für die Fertigung von Karosserieteilen in Presswerken auf Basis neuronaler Netze, Technische Universität München, PhD. Thesis, (2012).

Google Scholar

[10] I. Faaß, Prozessregelung für die Fertigung von Karosseriebauteilen in Presswerken, Technische Universität München, PhD. Thesis, (2009).

Google Scholar

[11] D. Hortig, Effizienzsteigerung in der Blechumformung: Integration der Qualitätsprozesse zur Steigerung der Prozessrobustheit, Produktivität und Flexibilität, in: Münchener Kolloquium Conference on Future Automotive Technology, TU München, (2014).

Google Scholar

[12] D. Hortig, Experiences with the robustness of sheet metal forming processes, in: P. Hora (Eds. ), Zero Failure Production Methods, Proceedings of the 4th Forming Technology Forum 2011, Zurich, 2011, pp.19-24.

Google Scholar

[13] R.C. Fenn, Closed-Loop Control of Forming Stability during Metal Stamping, Massachusetts Institute of Technology, PhD Thesis, (1989).

Google Scholar

[14] O. Straube, Untersuchungen zum Aufbau einer Prozessregelung für das Ziehen von Karosserieteilen, Technische Universität Berlin, PhD Thesis, (1994).

Google Scholar

[15] R. Neugebauer, H. Bräunlich, S. Scheffler, Process monitoring and closed loop controlled process, in: Archives of civil and mechanical engineering Vol. 9 No. 2 (2009) 105-126.

DOI: 10.1016/s1644-9665(12)60063-6

Google Scholar

[16] H. Müllerschön, W. Roux, D. Lorenz, K. Roll, Stochastic Analysis of Uncertainities for Metal Forming Processes with LS-OPT, in: Numisheet - international conference, 2008, pp.819-828.

Google Scholar

[17] J.H. Wiebenga, E.H. Atzema, Y.G. An, H. Vegter, A.H. van den Boogaard, Effect of material scatter on plastic behavior and stretchability in sheet metal forming, in: Journals of Materials Processing Technology 214 (2014) 238-252.

DOI: 10.1016/j.jmatprotec.2013.08.008

Google Scholar

[18] H. Hoffmann, M.F. Zäh, I. Faas, R. Mork, M. Golle, B. Griesbach, M. Kerschner, Automatic Process Control in Press Shops, in: Key Engineering Materials Vol. 344, 2007, pp.881-888.

DOI: 10.4028/www.scientific.net/kem.344.881

Google Scholar

[19] C. Annen, V. Gödel, P. Hora, M. Merklein, B. Oberpriller, Einfluss von Chargenschwankungen in Abhängigkeit des Prozesszustands, wt Werkstatttechnik online 100 (2010) 440-445.

DOI: 10.37544/1436-4980-2010-5-440

Google Scholar

[20] V. Sturm, Einfluss von Chargenschwankungen auf die Verarbeitungsgrenzen von Stahlwerkstoffen, FAU Erlangen, PhD. Thesis, (2013).

Google Scholar

[21] A. Neumann, Konzept zur Beherrschung der Prozessschwankungen im Presswerk, FAU Erlangen, PhD. Thesis, (2013).

Google Scholar

[22] K. Grossenbacher, Virtuelle Planung der Prozessrobustheit in der Blechumformung, ETH Zürich, PhD. Thesis, (2008).

Google Scholar

[23] V. Matyuk, A. Оsipov, M. Delendick, H. Hartmann, H. Reichelt, Testing of mechanical Properties of the light-gauge rolled Products of low-carbon Steels in the Zinc galvanizing line by the pulse magnetic method, Institute of Applied Physics of National Academy of Sciences of Belarus, Forschungs- und Qualitätszentrum Brandenburg, (2004).

Google Scholar

[24] K. Herrmann, M. Irle, IMPOC: an online material properties measurement system, in: V. B. Ginzburg (Eds. ), Flat-Rolled Steel Processes. Advanced Technologies, CRC Press, Boca Raton, 2009, pp.265-269.

DOI: 10.1201/9781420072938-c24

Google Scholar

[25] B. Heutling, W. Reimche, F.W. Bach, M. Stock, J. Kroos, M. Stolzenberg, S. Schulz, Online Material Characterisation of steel sheets in hot-dip galvanizing Lines by means of harmonic analysis of eddy current signals, in: Proceedings of the 16th World Conference on Nondestructive Testing (WCNDT), Montreal, (2004).

DOI: 10.3233/jae-2004-606

Google Scholar

[26] Information on http: /www. emg-automation. com (25. 11. 2014).

Google Scholar

[27] G. Moreas, W. Van De Velde, J. Bilstein, Advanced sensor for on-line topography in continuous annealing lines, in: Revue de Métallurgie 103 (2006) 233-237.

DOI: 10.1051/metal:2006111

Google Scholar

[28] W. Bilstein, G. Enderle, D. Moreas, T. Oppermann, F. Routschek, F. Van de Velde, Two Systems for On-Line Oilfilm and Surface Roughness Measurement for Strip Steel Production, in: Proceedings of the 4th European Steel Rolling Conference, Paris, (2006).

DOI: 10.1051/metal:2007111

Google Scholar

[29] N. Petsch, Lubrication of sheet strip and panels, Stahl-Informations-Zentrum, (2006).

Google Scholar

[30] H. Schöning, M. Dorchain, Data Mining und Analyse, in: T. Bauernhansl, M. Hompel, B. Vogel–Heuser (Eds. ), Industrie 4. 0 in Produktion, Automatisierung und Logistik, Springer–Verlag, Wiesbaden, 2014, pp.543-553.

DOI: 10.1007/978-3-658-04682-8_27

Google Scholar

[31] T. Bürger, K. Tragl, SPS-Automatisierung mit den Technologien der IT-Welt verbinden, in: T. Bauernhansl, M. Hompel, B. Vogel–Heuser (Eds. ), Industrie 4. 0 in Produktion, Automatisierung und Logistik, Springer–Verlag, Wiesbaden, 2014, pp.559-568.

DOI: 10.1007/978-3-658-04682-8_28

Google Scholar

[32] A. Birkert, S. Haage, M. Straub, Umformtechnische Herstellung komplexer Karosseriebauteile – Auslegung von Ziehanlagen, first ed., Springer-Vieweg-Verlag Berlin Heidelberg, (2013).

DOI: 10.1007/978-3-642-34670-5

Google Scholar

[33] S. Rittmeier, Systemunterstützte Umformung, Technische Universität Dresden, PhD. Thesis, (2007).

Google Scholar

[34] A. Neumann, D. Hortig, M. Merklein, Measurement of Material Flow in Series Production, in: Key Engineering Materials, Proceedings of the 14th International Conference on Sheet Metal, Leuven, 2011, pp.137-144.

DOI: 10.4028/www.scientific.net/kem.473.137

Google Scholar

[35] C. –W. Hsu, A.G. Ulsoy, M. Y. Demeri, An Approach for Modeling Sheet Metal Forming for Process Controller Design, in: Journal of Manufacturing Science and Engineering, Transactions of the ASME 122 (2000) 717-724.

DOI: 10.1115/1.1286815

Google Scholar

[36] Y Lim, R. Venugopal, A.G. Ulsoy, Advances in the Control of Sheet Metal Forming, in Proceedings of the 17th IFAC World Congress, Soul, 2008, pp.1875-1883.

DOI: 10.3182/20080706-5-kr-1001.00320

Google Scholar

[37] C. Blaich, Robuster Tiefziehprozess durch Erfassung und Optimierung der örtlichen Bauteilqualität, Universität Stuttgart, PhD. Thesis, (2012).

Google Scholar