Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

Schulze, Patti; Schmidl, E.; Grund, Thomas; Lampke, Thomas

doi:10.1088/1757-899x/118/1/012029

Cited by 2 publications

(2 citation statements)

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“…Both features were related to the dilatation strain energy induced by austenite-martensite transformation, which contributed to most of the non-chemical driving force of the austenite-martensite phase transformation. For bainite transformation start temperature prediction, it was found that the best model was created with MAE = 17.34, RMSE = 24.67 and R2 = 0.913, considering element characteristics in the form as shown in Table 19 [77]. In this work, it was found that atomic Waber-Crome pseudopotential radius comes first in the ranking of feature importance influencing the BS temperature.…”

Section: The Generalization Ability Of the Trained Modelsmentioning

confidence: 75%

“…For bainite transformation start temperature prediction, it was found that the best model was created with MAE = 17.34, RMSE = 24.67 and R2 = 0.913, considering element characteristics in the form as shown in Table 19 [ 77 ]. In this work, it was found that atomic Waber–Crome pseudopotential radius comes first in the ranking of feature importance influencing the BS temperature.…”

Section: Discussionmentioning

confidence: 99%

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Phase Transformation Temperature Prediction in Steels via Machine Learning

Zhang,

Cheng,

Pan

et al. 2024

Materials

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The phase transformation temperature plays an important role in the design, production and heat treatment process of steels. In the present work, an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation temperatures, namely Ac1, Ac3, the martensite transformation start (MS) temperature and the bainitic transformation start (BS) temperature. The effects of the alloying element were discussed in detail by comparing their influencing mechanisms on different phase transformation temperatures. The training accuracy was significantly improved by further introducing appropriate features related to atomic parameters. The melting temperature and coefficient of linear thermal expansion of the pure metals corresponding to the alloying elements, atomic Waber–Cromer pseudopotential radii and valence electron number were the top four among the eighteen atomic parameters used to improve the trained model performance. The training and prediction processes were analyzed using a partial dependence plot (PDP) and Shapley additive explanation (SHAP) methods to reveal the relationships between the features and phase transformation temperature.

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Section: The Generalization Ability Of the Trained Modelsmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%