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  4. A review of machine learning for the optimization of production processes
 
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2019
Journal Article
Titel

A review of machine learning for the optimization of production processes

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.
Author(s)
Weichert, Dorina
Link, Patrick
Fraunhofer-Institut fĂ¼r Werkzeugmaschinen und Umformtechnik IWU
Stoll, Anke
Fraunhofer-Institut fĂ¼r Werkzeugmaschinen und Umformtechnik IWU
RĂ¼ping, Stefan
Ihlenfeldt, Steffen
Fraunhofer-Institut fĂ¼r Werkzeugmaschinen und Umformtechnik IWU
Wrobel, Stefan
Zeitschrift
The International Journal of Advanced Manufacturing Technology
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DOI
10.1007/s00170-019-03988-5
Language
English
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Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Fraunhofer-Institut fĂ¼r Werkzeugmaschinen und Umformtechnik IWU
Tags
  • machine learning

  • optimization

  • manufacturing

  • production

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