TY - CHAP A1 - Böhm, Stefan-Andreas ED - Tomforde, Sven ED - Krupitzer, Christian T1 - Adaptable Machining Process Identification based on Expert Knowledge and Artificial Intelligence T2 - Organic Computing Doctoral Dissertation Colloquium 2021 N2 - The demand for product individuality increased enormously in recent years and thus affects directly manufacturers and their employees. Due to the increasing demand for batch-size-one production, every product needs specific manufacturing processes. Usually, employees determine these manufacturing steps with provided product data. This research proposal aims to contribute to the extraction of machining processes from product data and their assignment to suitable machinery. We plan to develop an organic computing system based on artificial intelligence methods to solve these problems by including customer-specific designs and employee expertise. The overall objective is to support employees in manufacturing facilities by simplifying the manufacturer and customer interaction. KW - organic computing KW - artificial intelligence, KW - reinforcement learning KW - internet of things KW - manufacturing systems Y1 - 2022 U6 - https://doi.org/10.17170/kobra-202202215780 SP - 1 EP - 14 PB - kassel university press CY - Kassel ER -