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The tempering response in the heat-affected zone (HAZ) of low alloy steels during temper bead welding is heavily dependent on the experienced thermal history. Past work has developed quantification approaches for isothermal tempering conditions and single non-isothermal tempering cycles, whereas the temper bead welding processes impart multiple non-isothermal cycles throughout the HAZ. This work outlines a novel methodology for tempering response quantification that allows for prediction of the HAZ hardness in multipass welding. The quantification approach utilizes a modification of the Grange-Baughman tempering parameter that converts non-isothermal cycles into an equivalent isothermal cycle and correlate this with the resulting hardness. This relationship can be utilized to evaluate hardness distributions throughout the HAZ of low alloy steel temper bead weldments based on the experienced thermal histories. It was shown that, in contrast with conventional heat treatment, the temper bead welding in Grade 22 steel results in nucleation of high density, finely dispersed Fe-Cr rich carbides. The proposed methodology was applied for evaluation of the HAZ hardness in a particular heat of Grade 22 steel, resulting from multiple tempering reheats, and was experimentally validated using a three-layer weld overlay. It was found that the peak temperature of weld tempering cycles was the most significant factor in controlling HAZ hardness.
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Tempered Hardness Optimization of Martensitic Alloy Steels
A simple Gaussian process regressor (GPR) model is employed to predict steel hardness and toughness response for tempered martensitic steels. A dataset of over 2000 hardness values from over 250 distinct alloys was compiled, with the aim of incorporating a diverse set of quenched and tempered martensitic steels. The Izod impact toughness was included for over 450 of these alloy/temper conditions. The GPR exhibited an increase in accuracy for both the predicted hardness and Izod impact toughness over linear regression trained on the same dataset. Shapley additive explanations (SHAP) were used to assess the importance of the input features of tempering temperature, tempering time, and 15 elements. Tempering temperature and carbon content were the most important input features in all models. The relative importance of the other 14 alloying elements varied depending on the target property. The SHAP analysis highlighted the complex relationships between composition and mechanical properties that are able to be captured by machine learning approaches.
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- Award ID(s):
- 2316628
- PAR ID:
- 10523521
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Integrating materials and manufacturing innovation
- ISSN:
- 2193-9772
- Subject(s) / Keyword(s):
- Tempered steel · Izod impact toughness · Shapley additive explanations · Martensitic steel
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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