学術雑誌論文

学術雑誌論文

text

Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science. 2022, 53(12), p. 4519-4534

Hardness Prediction System for Multi-pass Weld Metal of Low-Alloy Steel Using Neural Network

Springer Nature

英語

text

2022-12-01

https://doi.org/10.1007/s11661-022-06853-5

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/https://doi.org/10.1007/s11661-022-06853-5.

Multi-pass welds are subjected to various thermal cycles and have a complicated microstructure distribution; therefore, defects can easily occur. To prevent the defects, it is important to select the appropriate welding conditions before welding. Hardness is the most convenient criterion for the safety evaluation. A hardness prediction system for the heat-affected zone (HAZ) of a multi-pass weld has been proposed by the authors. However, in actual welding, defects can occur not only in the HAZ but also in the weld metal (WM) of a multi-pass weld. Therefore, in this study, hardness prediction system for the WM of a multi-pass weld was developed based on a neural network and database of experimental measurements, a new hardness prediction system of WM has been developed by using neural network. In addition, a hardness prediction system for an entire multi-pass weld, including both the WM and HAZ, was developed by combining the new system with the previously proposed hardness prediction system for the HAZ. Therefore, the hardness values for both the WM and HAZ could be predicted based on the simulated thermal cycles of multi-pass welding, and the calculated values were found to be in good agreement with the measured results. This indicated that the newly developed hardness prediction system for multi-pass welds, including both the WM and HAZ, was effective for selecting appropriate welding conditions prior to actual multi-pass welding.

89880

Hardness Prediction System for Multi-pass Weld Metal of Low-Alloy Steel Using Neural Network

Springer Nature

英語

2022-12-01

Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science

53

12

4519

4534

https://doi.org/10.1007/s11661-022-06853-5

https://hdl.handle.net/11094/89880