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Automated machine learning for predictive quality in production

 
: Krauß, J.; Pacheco, B.M.; Zang, H.M.; Schmitt, R.H.

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Volltext ()

Procedia CIRP 93 (2020), S.443-448
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <53, 2020, Online>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPT ()

Abstract
Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan.

: http://publica.fraunhofer.de/dokumente/N-614739.html