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Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning
The continuous cooling transformation (CCT) diagram of steels is very important in considering the phase transformation depending on the cooling rate of a material; however, it is difficult to obtain the diagram for each steel because of much experimental effort required. Therefore, it is important to establish a technique to predict the CCT diagram with good accuracy under arbitrary conditions such as composition and cooling rate. We have developed a prediction model of a CCT diagram for the weld heat affected zone (HAZ) using machine learning based on existing experimental data. The prediction accuracy was improved by separately considering critical cooling rate and temperature at which the transformation starts at various cooling rates, and by using double cross-validation (DCV) to effectively use a small amount of data.
Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning
The continuous cooling transformation (CCT) diagram of steels is very important in considering the phase transformation depending on the cooling rate of a material; however, it is difficult to obtain the diagram for each steel because of much experimental effort required. Therefore, it is important to establish a technique to predict the CCT diagram with good accuracy under arbitrary conditions such as composition and cooling rate. We have developed a prediction model of a CCT diagram for the weld heat affected zone (HAZ) using machine learning based on existing experimental data. The prediction accuracy was improved by separately considering critical cooling rate and temperature at which the transformation starts at various cooling rates, and by using double cross-validation (DCV) to effectively use a small amount of data.
Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning
Satoshi Minamoto (author) / Susumu Tsukamoto (author) / Tadashi Kasuya (author) / Makoto Watanabe (author) / Masahiko Demura (author)
2022
Article (Journal)
Electronic Resource
Unknown
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