Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 13, November 2019

Prediction of Workpiece Quality: An Application of Machine Learning in Manufacturing Industry


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