Trends of machine learning in additive manufacturing. (2018)

• Record Type: Journal Article
• Authors: Baumann, Felix W.; Sekulla, André; Hassler, Michael; Himpel, Benjamin; Pfeil, Markus
• Is Part Of: International journal of rapid manufacturing. Volume 7:Number 4(2018)
• Page Start: 310
• Page End: 336
• Publication Date: 2018
• Subjects: survey -- review -- AM -- additive manufacturing -- ML -- machine learning -- deep learning -- artificial neural network; Manufacturing processes -- Periodicals; 670.4205
• Languages: English
• ISSNs: 1757-8817

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.