Machine Learning in Production – Potentials, Challenges and Exemplary Applications

Mayr A, Kißkalt D, Meiners M, Lutz B, Schäfer F, Seidel R, Selmaier A, Fuchs J, Metzner M, Blank A, Franke J (2019)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2019

Pages Range: 49-54

Conference Proceedings Title: Procedia CIRP

Event location: Online


DOI: 10.1016/j.procir.2020.01.035


Recent trends like autonomous driving, natural language processing, service robotics or Industry 4.0 are mainly based on the tremendous progress made in the field of machine learning (ML). The increased data availability coupled with affordable computing power and easy-to-use software tools have laid the foundation for using such algorithms in a wide range of industrial applications, e.g. for predictive maintenance, predictive quality or machine vision. However, a systematic guideline for identifying and implementing economically viable ML use cases in manufacturing industry is still missing. In particular, there is still a lack of a structured overview of concrete, industry-specific best practices that can be easily transferred to one’ s own production. Hence, this paper aims to summarize various existing application scenarios of ML from a process and an industry sector perspective. The process point of view mainly covers the main manufacturing process groups of DIN 8580, handling operations according to VDI 2860 as well as selected cross-process approaches. From an industry sector perspective, application scenarios from various subsectors such as the production of electronics, electric motors, transmission components and medical devices are outlined.

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How to cite


Mayr, A., Kißkalt, D., Meiners, M., Lutz, B., Schäfer, F., Seidel, R.,... Franke, J. (2019). Machine Learning in Production – Potentials, Challenges and Exemplary Applications. In Procedia CIRP (pp. 49-54). Online.


Mayr, Andreas, et al. "Machine Learning in Production – Potentials, Challenges and Exemplary Applications." Proceedings of the 7th CIRP Global Web Conference – Towards shifted production value stream patterns through inference of data, models, and technology (CIRPe 2019), Online 2019. 49-54.

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