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

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.

[1]  A. H. van den Boogaard,et al.  Effect of material scatter on the plastic behavior and stretchability in sheet metal forming , 2014 .

[2]  Vivek Shrivastava,et al.  Artificial Neural Network Based Optical Character Recognition , 2012, ArXiv.

[3]  Bernhard Mitschang,et al.  Data Mining-driven Manufacturing Process Optimization , 2012 .

[4]  Marc Dorchain,et al.  Data Mining und Analyse , 2014 .

[5]  Ralph Christian Fenn Closed-loop control of forming stability during metal stamping , 1989 .

[6]  Marion Merklein,et al.  Measurement of Material Flow in Series Production , 2011 .

[7]  Karl Roll,et al.  STOCHASTIC ANALYSIS OF UNCERTAINTIES FOR METAL FORMING PROCESSES WITH LS-OPT , 2008 .

[8]  Kathrin Grossenbacher Virtuelle Planung der Prozessrobustheit in der Blechumformung , 2008 .

[9]  A. Wagner,et al.  Advanced sensor for on-line topography in continuous annealing lines , 2006 .

[10]  Thomas Bürger,et al.  SPS-Automatisierung mit den Technologien der IT-Welt verbinden , 2017, Handbuch Industrie 4.0.

[11]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[12]  Reimund Neugebauer,et al.  Process monitoring and closed loop controlled process , 2009 .

[13]  A. Galip Ulsoy,et al.  An Approach for Modeling Sheet Metal Forming for Process Controller Design , 2000 .

[14]  Ben Tse,et al.  Autonomous Inverted Helicopter Flight via Reinforcement Learning , 2004, ISER.

[15]  Peter Groche,et al.  Industrie 4.0 - Chance auch für die Umformtechnik? , 2014 .

[16]  W. Enderle,et al.  Two systems for on-line oil film and surface roughness measurement for strip steel production , 2007 .

[17]  Michael F. Zäh,et al.  Automatic Process Control in Press Shops , 2007 .

[18]  Yongseob Lim,et al.  Advances in the Control of Sheet Metal Forming , 2008 .

[19]  Klaus Herrmann,et al.  IMPOC©: An Online Material Properties Measurement System , 2009 .