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Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME)Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels

Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME):... [Establishing correlations between various properties of alloys and their compositions and manufacturing process parameters is of significant interest to materials engineers. Both physics-based as well as data-driven approaches have been used in pursuit of this. Of various properties of interest, fatigue strength, being an extreme value property, had only a limited amount of success with physics based models. In this paper, we explore a systematic data driven approach, supplemented by physics based understanding, employing various regression methods with dimensionality reduction and machine learning methods applied to the fatigue properties of steels available from the National Institute of Material Science public domain database to arrive at correlations for fatigue strength of steels and present an assessment of the residual errors in each method for comparison. This study is expected to provide insights into the methods studied to make objective selection of appropriate method.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME)Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels

Editors: Li, Mei; Campbell, Carelyn; Thornton, Katsuyo; Holm, Elizabeth; Gumbsch, Peter
Deshpande, P. D.; Gautham, B. P.; Cecen, A.; Kalidindi, S.; Agrawal, A.; Choudhary, A.
Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME) — Oct 3, 2016
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References (7)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2016
ISBN
978-3-319-48585-0
Pages
155–160
DOI
10.1007/978-3-319-48194-4_25
Publisher site
See Chapter on Publisher Site

Abstract

[Establishing correlations between various properties of alloys and their compositions and manufacturing process parameters is of significant interest to materials engineers. Both physics-based as well as data-driven approaches have been used in pursuit of this. Of various properties of interest, fatigue strength, being an extreme value property, had only a limited amount of success with physics based models. In this paper, we explore a systematic data driven approach, supplemented by physics based understanding, employing various regression methods with dimensionality reduction and machine learning methods applied to the fatigue properties of steels available from the National Institute of Material Science public domain database to arrive at correlations for fatigue strength of steels and present an assessment of the residual errors in each method for comparison. This study is expected to provide insights into the methods studied to make objective selection of appropriate method.]

Published: Oct 3, 2016

Keywords: Material Informatics; Regression Analysis; Processing-Property Linkages; Artificial Neural Networks

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