Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms

In this study, the ability of machine learning algorithms to predict tensile properties of both heat-treated and non-heat treated LPBFed AlSi10Mg alloy is investigated. The data was analyzed using various Machine Learning Regression (MLR) models such as Linear Regression (LR), Gaussian Process Regre...

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Veröffentlicht in:	PloS one Jg. 20; H. 6; S. e0324049
Hauptverfasser:	Jatti, Vijaykumar S., Saiyathibrahim, A., Yadav, Arvind, R., Murali Krishnan, Jayaprakash, B., Kaushal, Sumit, Jatti, Vinaykumar S., Jatti, Ashwini V., Jatti, Savita V., Kumar, Abhinav, Gouadria, Soumaya, Bonyah, Ebenezer
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 02.06.2025 Public Library of Science (PLoS)
Schlagworte:	3-D printers Additive manufacturing Algorithms Alloys Alloys - chemistry Aluminum alloys Aluminum base alloys Analysis Aviation Biology and Life Sciences CAD Composition Computer aided design Computer and Information Sciences Decision trees Engineering and Technology Forecasts and trends Gaussian process Heat Heat treatment Hot Temperature Lasers Learning algorithms Machine Learning Magnesium - chemistry Materials Testing Mechanical properties Methods Neural networks Physical Sciences Product development Regression Regression analysis Research and Analysis Methods Root-mean-square errors Solidification Stainless steel Strength of materials Tensile properties Tensile Strength Thermal properties India
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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