Optimization of cold rolling process recipes based on historical data

Abstract

Cold rolling is a process in sheet metal production used to reduce thickness, standardise thickness, and ensure suitable mechanical properties of the workpiece. In order to ensure the appropriate product quality, it is extremely important to set the rolling mill correctly, which is usually determined by predefined recipes. In this paper, a decision support tool is proposed that enables recipe adjustment for individual workpieces. It is based on the analysis of historical process data and its impact on the associated key performance indicators (KPIs). The support tool includes a data-based process model, a multi-criteria optimization algorithm and a user interface.