Günther Schuh¹, Paul Scholz², Sebastian Schorr³, Durmus Harman², Matthias Möller⁴, Jörg Heib⁴ and Dirk Bähre³, ¹RWTH Aachen University Aachen, Germany, ²Fraunhofer Institute for Production Technology, RWTH Aachen University, Germany, ³Saarland University, Germany and ⁴Bosch Rexroth AG, Germany

A significant amount of data is generatedand could be utilized in order to improve quality, time, and cost related performance characteristics of the production process. Machine Learning (ML) is considered as a particularly effective method of data processing with the aim of generating usable knowledge from data and therefore becomes increasingly relevant in manufacturing. In this research paper, a technology framework is created that supports solution providers in the development and deployment process of ML applications. This framework is subsequently successfully employed in the development of an ML application for quality prediction in a machining process of Bosch Rexroth AG.For this purpose the 50 mostrelevant features were extracted out of time series data and used to determine the best ML operation. Extra Tree Regressor (XT) is found to achieve precise predictions with a coefficient of determination (R 2 ) of constantly over 91% for the considered quality characteristics of a boreof hydraulic valves.

Technology Management Framework, Quality Prediction, Machine Learning, Manufacturing, Workpiece Quality