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The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 5-8/2019

20.06.2019 | ORIGINAL ARTICLE

A review of machine learning for the optimization of production processes

verfasst von: Dorina Weichert, Patrick Link, Anke Stoll, Stefan Rüping, Steffen Ihlenfeldt, Stefan Wrobel

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 5-8/2019

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Abstract

Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.
Vorheriger Artikel Extending lean frontiers: a kaizen case study in an Italian MTO manufacturing company
Nächster Artikel On the performance of CUSUM control charts for monitoring the coefficient of variation with measurement errors

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Metadaten
Titel
A review of machine learning for the optimization of production processes
verfasst von
Dorina Weichert
Patrick Link
Anke Stoll
Stefan Rüping
Steffen Ihlenfeldt
Stefan Wrobel
Publikationsdatum
20.06.2019
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 5-8/2019
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-019-03988-5

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