Abstract
Ensuring high product quality is an important success factor in modern industry. Data-driven models are increasingly used for this purpose and need to be integrated into industrial processes as early as possible. As these models require high-quality training data, strategies for obtaining such data in the commissioning phase of a process are needed. Design of experiments (DoE) methods can be used for this task, but are not directly applicable for all types of processes. A common class of processes, to which most DoE methods cannot be applied, are assembly processes. The approach described in this paper aims to gather as much information as possible from an assembly process with minimal effort. It makes use of given sets of components from which optimal combinations are created, in order to achieve a space-filling design. Further design improvements are achieved by additionally specifying different mounting position of parts in the design and by using a subset selection procedure to remove less informative design points. The approach was successfully applied to an assembly process of washing machine drums to predict the radial deviation of assembled drums.
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Acknowledgements
This work was supported by the EFRE-NRW funding program “Forschungsinfrastrukturen” (grant no. 34.EFRE-0300180). The authors would like to thank Miele & Cie. KG for enabling the experiments.
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Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_37
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