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Process control in a press hardening production line with numerous process variables and quality criteria

Prozesskontrolle in der Blechwarmumformung mit multiplen Prozessvariablen und Qualitätskriterien
: Stoll, Anke; Pierschel, Norbert; Wenzel, Ken; Langer, Tino

Fulltext ()

Beyerer, J.:
Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2018 : Selected papers from the International Conference ML4CPS 2018, Karlsruhe, October 23rd and 24th, 2018
Berlin: Springer Vieweg, 2019 (Technologies for Intelligent Automation 9)
ISBN: 978-3-662-58484-2 (Print)
ISBN: 978-3-662-58485-9 (Online)
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <4, 2018, Karlsruhe>
Conference Paper, Electronic Publication
Fraunhofer IWU ()
linear regression; least squares optimization; Production Line; press hardening; process control

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.