Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review

dc.creatorJoshi, M.S.
dc.creatorFlood, A.
dc.creatorSparks, T.
dc.creatorLiou, F.W.
dc.date.accessioned2021-11-16T16:17:50Z
dc.date.available2021-11-16T16:17:50Z
dc.date.issued2019
dc.description.abstractAdditive 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.en_US
dc.description.departmentMechanical Engineeringen_US
dc.identifier.urihttps://hdl.handle.net/2152/90331
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/17252
dc.language.isoengen_US
dc.publisherUniversity of Texas at Austinen_US
dc.relation.ispartof2019 International Solid Freeform Fabrication Symposiumen_US
dc.rights.restrictionOpenen_US
dc.subjectsupervised learning algorithmsen_US
dc.subjectsupport vector machinesen_US
dc.subjectrandom forestsen_US
dc.subjectmachine learningen_US
dc.subjectadditive manufacturingen_US
dc.titleApplications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Reviewen_US
dc.typeConference paperen_US

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