Data driven modeling & optimization of industrial processes

Stein, B. van (2018)

Doctoral Thesis

Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality indicators.
To optimize these complex processes, for example by reducing the number of defects or increasing the throughput, a great number of requirements need to be taken into consideration.
In this dissertation a framework for monitoring and optimizing these complex industrial processes is presented. The framework is specifically tailored to the production processes of Tata Steel and BMW Group. Both are industrial partners of the PROMIMOOC project.
The framework consists of several components of which; preprocessing, outlier detection, predictive modeling and optimization are the main technical components that are the focus of this work. For each of these components a possible implementation is proposed and the challenges in implementing these components in an industrial manufacturing setting are discussed

• Machine learning
• Data mining
• Industrial processes
• Optimization
• Kriging
• Quassian processes
• Data driven
• Surrogate models

• All authors: Stein, B. van
• Supervisor: Bäck, T.H.W.
• Co-supervisor: Kowalczyk, W.J.
• Committee: Plaat, A.; Verbeek, F.J.; Hoos, H.H.; Kleijn, H.C.M.; Manegold, S.; Filipic, B.; Mehnen, J.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2018-09-20
• Sponsorship: BMW Tata Steel MonetDB