Title: Trends of machine learning in additive manufacturing

Authors: Felix W. Baumann; André Sekulla; Michael Hassler; Benjamin Himpel; Markus Pfeil

Addresses: Institut für Rechnergestützte Ingenieursysteme, Universität Stuttgart, Universitätsstr. 38, D-70569 Stuttgart, Germany ' Computerunterstützte Gruppenarbeit und Soziale Medien, Universität Siegen, Kohlbettstr. 15, D-57068 Siegen, Germany ' TWT GmbH Science and Innovation, Ernsthaldenstr. 17, D-70565 Stuttgart, Germany ' TWT GmbH Science and Innovation, Ernsthaldenstr. 17, D-70565 Stuttgart, Germany ' Hochschule Ravensburg-Weingarten, Doggenriedstr, D-88250 Weingarten, Germany

Abstract: In this work, the influence on and application of machine learning (ML) to the domain of Additive Manufacturing or synonymously 3D printing is reviewed. Existing literature is identified by a literature search and grouped according to its application in 3D printing. We provide insight into this research and the potential of ML, deep learning, and other related computational learning methods on additive manufacturing (AM) and its potential future development and embedding, such as cloud manufacturing or Industry 4.0. The application of ML is discussed to aid solving numerous problems from Additive Manufacturing, such as process control, process monitoring, and quality enhancement of manufactured objects. Furthermore, literature covering the generalities of the intersection of Additive Manufacturing and Machine Learning, reviews and future research questions are identified and presented herein. This work provides an overview of the benefits and drawbacks of combining Additive Manufacturing with Machine Learning.

Keywords: survey; review; AM; additive manufacturing; ML; machine learning; deep learning; artificial neural network.

DOI: 10.1504/IJRAPIDM.2018.095788

International Journal of Rapid Manufacturing, 2018 Vol.7 No.4, pp.310 - 336

Received: 08 Sep 2017
Accepted: 24 Jan 2018
Published online: 22 Oct 2018 *

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