片状黒鉛鋳鉄の微量成分を考慮したディープラーニングによる機械的性質予測

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  • Mechanical Properties Prediction of Gray Cast Iron Considering Trace Elements Based on Deep Learning
  • ヘンジョウ コクエン チュウテツ ノ ビリョウ セイブン オ コウリョ シタ ディープラーニング ニ ヨル キカイテキ セイシツ ヨソク

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<p>  Except in the case of martensitic transformation during quenching and age-hardening, the mechanical properties (tensile strength and hardness) of many metallic materials are often determined by its chemical composition. If mechanical properties can be predicted from the chemical composition of molten metal before casting, it can contribute to the stabilization of quality and the reduction of the testing process of tensile strength and hardness. In the case of gray cast iron, mechanical properties are often discussed with five main elements (C, Si, Mn, P and S). Multiple regression shows low performance in terms of correlation coefficient. Therefore, trace elements other than the five main elements should be considered since the influence of trace elements on mechanical properties is mostly nonlinear, making it difficult to analyze by multiple regression. Given that deep neural network (DNN) can take nonlinear cases into consideration, we investigated whether mechanical properties can be predicted from chemical compositions including trace elements, and obtained the following findings. For comparison, we also analyzed mechanical properties by multilayer perceptron (MLP) and multiple regression (MR). As a result, the prediction accuracy of DNN, MLP and MR improved by the consideration of not only the five main elements but also 18 other elements including trace elements. Prediction error of tensile strength analyzed by DNN was less than half of MR. Increasing the number of layers and the number of nodes in DNN improved the prediction accuracy of mechanical properties, demonstrating the effectiveness of DNN.</p>

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  • 鋳造工学

    鋳造工学 91 (5), 253-257, 2019-05-25

    公益社団法人 日本鋳造工学会

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