Abstract
Within the increasing digitalization, the widespread application of modern information and communication technologies and the technological ability to systematically and comprehensively capture and store data allow to build data storages of unprecedented size and quality. The evaluation and efficient use of the implicit knowledge in the data to support decision-making processes is becoming increasingly significant in manufacturing companies. Thus, new requirements arise for the qualification and competence development to efficiently solve engineering applications and issues in manufacturing and assembly with advanced data-driven methods. This paper presents the contribution of a qualification concept for Machine Learning in industrial production that has been realised within a recent research project funded by the Federal Ministry of Education and Research. This concept has been designed and validated within the university curriculum for graduate students in mechanical and industrial engineering, computer science and statistics. Taking into account the current challenges in manufacturing and assembly, the contribution of this enhanced interdisciplinary competence development can be considered quite significant. The results, findings, and future enhancements are presented within this paper.
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Schulte, L., Schmitt, J., Stankiewicz, L., Deuse, J. (2020). Industrial Data Science - Interdisciplinary Competence for Machine Learning in Industrial Production. In: Schüppstuhl, T., Tracht, K., Henrich, D. (eds) Annals of Scientific Society for Assembly, Handling and Industrial Robotics. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61755-7_15
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DOI: https://doi.org/10.1007/978-3-662-61755-7_15
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