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

Author:

Purr Stephan1,Meinhardt Josef2,Lipp Arnulf2,Werner Axel1,Ostermair Martin1,Glück Bernhard2

Affiliation:

1. BMW Plant Regensburg

2. BMW Group

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.

Publisher

Trans Tech Publications, Ltd.

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference37 articles.

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

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.

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).

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.

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).

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