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Data from Prediction for Mechanical Properties of Lean Duplex Stainless Steel by Using Random Forest

posted on 16.04.2022, 01:27 by Shoei OSAWA, Takao MIYOSHI, Pang-jo ChunPang-jo Chun

Lean duplex stainless steel (LDSS), which is expected to apply to infrastructures, exhibits rounded shape of stress-strain curve. For this reason, a constitutive equation which is able to accurately express the curve is required for the ultimate strength analysis of LDSS structures. Authours have already proposed MRO curve as this kind of equation. However, not only 0.2% proof stress and tensile strength, which are specified in common material standard and a mill certificate, but also mechanical properties such as proportion limit etc are needed to describe the equation. In this study, we collected tension coupon test results of LDSS and created the simple estimated equation by means of linear regression analysis. Also, we predicted the me- chanical properties by using Random Forest (RF) which is one of machine learning method. According to comparison predicted results by RF with those by estimated equation, it was revealed that RF has same prediction accuracy of mechanical properties as estimation equation.


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土木構造物への活用が期待されているリーン二相系ステンレス鋼(LDSS)は,ラウンドハウス型の応 力-ひずみ曲線を有するため,LDSS 構造物の耐荷力解析では,その応力-ひずみ曲線が精度よく表現で きる構成式が求められる.著者らは構成式として MRO 曲線を提案している.しかし,MRO 曲線の記述に は,一般的な材料規格やミルシートに与えられている 0.2%耐力や引張強度のみならず,比例限界などの機 械的特性値が必要となる.本研究では,LDSS の引張試験結果を収集し,線形回帰分析によりそれらの推 定式を作成した.また,機械学習の一つであるランダムフォレスト(RF)のこの種の問題への活用に着目 し,RF を用いてそれらを推定した.結果として,両推定結果の比較から,RF は推定式と同等の推定精度 を持つことが明らかになった.

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Translated authors

大澤 捷瑛, 三好 崇夫, 全 邦釘


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