Abstract
Today, the optimization of the press hardening process is still a complex and challenging task. This report describes the combination of linear regression with least squares optimization to adjust the process parameters of this process for quality improvement. The FE simulation program AutoForm was used to model the production line concerned and various process and quality parameters were measured. The proposed system is capable of automatically adjusting the process parameters of following process steps based on the quality estimate at each step of the production line. An additional benefit is the identification of likely defective parts early in the production process. Based on the results derived from 1000 observations a better understanding of the process was obtained and in the future the combined regression and optimization approach can be extended to more complex production lines.
Keywords
- linear regression
- least squares optimization
- production line
- press hardening
- process control
Download conference paper PDF
References
Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. Journal of Industrial Information Integration 6, 1–10 (2017)
Harding, J.A., Shahbaz, M., Kusiak, A.: Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering 128.4, 969–976 (2006)
Niggemann, O., Stein, B., Maier, A.: Solving Modeling Problems with Machine Learning A Classification Scheme of Model Learning Approaches for Technical Systems. In Model-Based Development of Embedded Systems (MBEES), Dagstuhl (2012)
Oh, S., Han, J., Cho, H.: Intelligent process control system for quality improvement by data mining in the process industry. Data mining for design and manufacturing. Springer US, 289–309 (2001)
Senn, M., Link, N.: A universal model for hidden state observation in adaptive process controls. International Journal on Advances in Intelligent Systems 4(3-4), 245–255 (2012)
AutoForm, url: https://www.autoform.com/
Neugebauer, R., Schieck, F., Polster, S., Mosel, A., Rautenstrauch, A., Sch¨onherr, J., Pierschel, N.: Press hardening An innovative and challenging technology. Archives of civil and mechanical engineering 12(2), 113–118 (2012)
Ihaka, R., Gentleman, R.: R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5(3), 299 – 314 (1996)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/(2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2019 The Author(s)
About this paper
Cite this paper
Stoll, A., Pierschel, N., Wenzel, K., Langer, T. (2019). Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-662-58485-9_9
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-58484-2
Online ISBN: 978-3-662-58485-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)