This thesis focuses on the problem of using historical production data for the purpose of product quality improvement. The subject of the discussion is cold rolling process where a detailed insight into the process conditions is available after an extensive digitalization of the process.
Cold rolling is one of the most important processes in sheet metal production and is used for reducing the thickness, making thickness uniform, and ensuring the appropriate mechanical properties of the workpiece. In order to ensure the appropriate quality of the product, it is extremely important to adjust the rolling mill properly, which is set according to the recipes and with manual interventions of the operator before and/or during rolling. The rules for a base recipe correction are usually experiential, which shows a significant impact of the operator on the final product quality.
For the purpose of achieving a higher product quality, a greater processing consistency, and a smaller influence of operators, the thesis proposes a support tool which is based on historical production data and real-time measurements of process variables. It also includes the appropriate process models, a simulation environment and the rules that suggest a more suitable base recipes correction.
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