MATERIALS TRANSACTIONS
Online ISSN : 1347-5320
Print ISSN : 1345-9678
ISSN-L : 1345-9678
Prediction of Fatigue Strength in Steels by Linear Regression and Neural Network
Takayuki ShiraiwaYuto MiyazawaManabu Enoki
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2018 Volume 60 Issue 2 Pages 189-198

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Abstract

This paper examines machine learning methods to predict fatigue strength with high accuracy using existing database. The fatigue database was automatically classified by hierarchical clustering method, and a group of carbon steels was selected as a target of machine learning. In linear regression analyses, a model selection was conducted from all possible combinations of explanatory variables based on cross validation technique. The derived linear regression model provided more accurate prediction than existing empirical rules. In neural network models, local and global sensitivity analyses were performed and the results of virtual experiments were consistent with existing knowledge in materials engineering. It demonstrated that the machine learning method provides prediction of fatigue performance with high accuracy and is one of promising method to accelerate material development.

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© 2018 The Japan Institute of Metals and Materials
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