Abstract
The digitalization in manufacturing offers high potential for optimization in terms of quality and efficiency. In particular, machine learning techniques can be used to analyze data generated along the production chain for complex patterns. As the final product quality highly depends on interactions within the production chain, a process control system needs to consider all information of the supplied semi-finished products to achieve a continuously high quality. Using machine learning, this work presents a two-stage batch control system to optimize a batch process with high interdependencies between the delivered material, used equipment and process parameters concerning the final product quality.
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