In this paper, we present a new framework for process optimizations, the Data Analytics Production Line Optimization Model (DAPLOM). Due to increasing efforts in the digitalization of production systems, an extensive amount of production data is available for analytics. This data can be used for the optimization of production lines and the prediction of their performance (e.g. drift of parameters or component quality) in order to achieve economic and technical improvements. The demand for systematical usage of data-driven methods involving technologies like Data Analytics and Machine Learning and the combination of engineering approaches is growing continuously. DAPLOM guides the implementation process of IT supported problem-solving solutions in production environments. It combines classical process- with data-driven approaches. Specific focus lies on achieving a holistic perspective with a macro- as well as a microscopic view on the given conditions. Here the macroscopic view covers the general material flow, whereas microscopic view considers process details. Additionally, DAPLOM provides useful methods in a step-by-step procedure structured in seven phases. The framework is validated in an industrial use case of an automated wire bending process. Thus, the effectiveness of the framework is demonstrated and further development potentials are identified.

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 (R2) of constantly over 91% for the considered quality characteristics of a boreof hydraulic valves.