Authors: Thorsten Wuest, Daniel Weimer, Christopher Irgens & Klaus-Dieter Thoben
Journal: Production & Manufacturing Research
Publisher: Taylor & Francis Group
The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application.
Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.
© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Illustration Photo: IBM researchers Ping Zhang (left) and Jianying Hu (right) have been granted U.S. Patent 9,536,194 for an invention that could accelerate discovery of more effective and safer drugs by using machine learning models to predict therapeutic indications and side effects from various drug information sources. (credits: IBM Research / Flickr Creative Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0))