Abstract
Companies are progressively gathering data within the digitalization of production processes. By actively using these production data sets operational processes can be improved, hence empowering businesses to compete with other corporations. One way to achieve this is to use data from production processes to develop and offer smart services that enable companies to continuously improve and to become more efficient. In this paper, the authors present an industrial use case of how machine learning can be implemented into smart services in production processes to decrease the time for resolving upcoming issues in manufacturing. The implementation is carried out by using an assistance system that aids a team which attends to problems in the assembling of turbines. Therefore, the authors have analyzed the assembly problems from an issue management system that the team had to resolve. Subsequently three different approaches based upon natural language processing, regression and clustering were selected. This paper also presents the development and evaluation of the assistance system.
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Brünnhäußer, J., Gogineni, S., Nickel, J., Witte, H., Stark, R. (2020). Assembly Issue Resolution System Using Machine Learning in Aero Engine Manufacturing. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems. APMS 2020. IFIP Advances in Information and Communication Technology, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-030-57993-7_18
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DOI: https://doi.org/10.1007/978-3-030-57993-7_18
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