Version 1
: Received: 27 January 2021 / Approved: 28 January 2021 / Online: 28 January 2021 (15:30:45 CET)
How to cite:
Liou, F.; Spark, M.; Flood, A.; Joshi, M. Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review. Preprints2021, 2021010588
Liou, F.; Spark, M.; Flood, A.; Joshi, M. Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review. Preprints 2021, 2021010588
Liou, F.; Spark, M.; Flood, A.; Joshi, M. Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review. Preprints2021, 2021010588
APA Style
Liou, F., Spark, M., Flood, A., & Joshi, M. (2021). Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review. Preprints. https://doi.org/
Chicago/Turabian Style
Liou, F., A Flood and M Joshi. 2021 "Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review" Preprints. https://doi.org/
Abstract
Additive Manufacturing (AM) simplifies the fabrication of complex geometries. Its scope has rapidly expanded from the fabrication of pre-production visualization models to the manufacturing of end use parts driving the need for better part quality assurance in the additively manufactured parts. Machine learning (ML) is one of the promising techniques that can be used to achieve this goal. Current research in this field includes the use of supervised and unsupervised ML algorithms for quality control and prediction of mechanical properties of AM parts. This paper explores the applications of supervised learning algorithms - Support Vector Machines and Random Forests. Support vector machines provide high accuracy in classifying the data and is used to decide whether the final parts have the desired properties. Random Forests consist of an ensemble of decision trees capable of both classification and regression. This paper reviews the implementation of both algorithms and analyzes the research carried out on their applications in AM.
Keywords
Additive Manufacturing; 3D printing
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.