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説明:

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.

権利情報:

キーワード: CCT

刊行年月日: 2022-12-31

出版者:

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 2 issue. 1 p. 402-415

研究助成金:

• JST (SIP)

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2022.2123262

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更新時刻: 2024-01-05 22:13:22 +0900

MDRでの公開時刻: 2023-02-08 16:18:57 +0900