Decision Support for the Optimization of Continuous Processes using Digital Shadows

Interdisciplinary excellence accelerator series, pp. 1–22

Abstract

Abstract Decision support systems can provide real-time process information and correlations, which in turn assists process experts in making decisions and thus further increase productivity. This also applies to well-established and already highly automated processes in continuous production employed in various industrial sectors. Continuous production refers to processes in which the produced material, either fluid or solid form, is continuously in motion and processed. As a result, the process can usually not be stopped. It is only possible to influence the running process. However, the highly nonlinear interactions between process parameters and product quality are not always known in their entirety which led to inferior product quality in terms of mechanical properties and surface quality. This requires accurate representations of the processes and the products in real-time, so-called digital shadows. Therefore, this contribution shows the necessary steps to provide a digital shadow based on numerical, physical models and process data and to couple the digital shadow with data analysis and machine learning to enable automatic decision support. This is exemplified at various stages throughout two different process chains with continuous processes: first, by using a thermoplastic production process called profile extrusion, and second, on the example of a metal processing process chain, from which three processes are described in more detail, namely, hot rolling, tempering, and fine blanking. Finally, the presented approaches and results are summarized.

Authors 9

1. Christian Idzik corresponding Aachen Institute of Metal of Forming

   RWTH Aachen University

   Affiliation as printed

   Institute of Metal of Forming, RWTH Aachen University, Aachen, Germany

2. Norbert Hosters Aachen Chair for Computational Analysis of Technical Systems (CATS)

   RWTH Aachen University

   Affiliation as printed

   Chair for Computational Analysis of Technical Systems, RWTH Aachen University, Aachen, Germany

3. Marco Kemmerling Aachen Information Management in Mechanical Engineering (IMA)

   RWTH Aachen University

   Affiliation as printed

   Information Management in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

4. Philipp Niemietz Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany

5. Lucia Ortjohann Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany

6. Jana Sasse Aachen Institute for Plastics Processing in Industry and Craft

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing in Industry and Craft, RWTH Aachen University, Aachen, Germany

7. Alexandros Serafeim Aachen Steel Institute

   RWTH Aachen University

   Affiliation as printed

   Steel Institute, RWTH Aachen University, Aachen, Germany

8. Jing Wang Aachen Steel Institute

   RWTH Aachen University

   Affiliation as printed

   Steel Institute, RWTH Aachen University, Aachen, Germany

9. Gerhard Hirt Aachen Institute of Metal of Forming

   RWTH Aachen University

   Affiliation as printed

   Institute of Metal of Forming, RWTH Aachen University, Aachen, Germany