出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
