Abstract

Accurate prediction of the critical transformation temperatures Ac1 and Ac3 is one of the essential factors in the heat treatment of CA6NM cast martensitic stainless steel. In this study, the influence of alloy composition, heating rate, and prior austenite grain size on the critical temperatures was quantified using dilatometric measurements and statistical modeling. Six heat treatment conditions produced prior austenite grain sizes ranging from ~75 to 247 µm. Experimental results showed that grain size variations produced only minor changes in Ac1 and Ac3, indicating a weak dependence of transformation temperatures on prior austenite grain size within the investigated range. In contrast, chemical composition and heating rate had strong effects, with Ac1 and Ac3 increasing by approximately 100 °C and 115 °C, respectively, as the heating rate increased from 0.1 to 10 °C s−1. Regression-based predictive model incorporating alloy composition and heating rate was developed and validated. The Ac1 model demonstrated strong agreement with experimental measurements (R2 = 0.972 and RMSE = 4.78 °C) and significantly improved prediction accuracy compared with thermodynamic calculations using Thermo-Calc and previously published empirical models. The results provide an improved framework for predicting transformation temperatures in CA6NM, under continuous heating conditions.

Department(s)

Materials Science and Engineering

Publication Status

Open Access

Comments

Defense Logistics Agency, Grant None

Keywords and Phrases

alloying element effects; austenite transformation; dilatometry; heat treatment; heating rates; modeling and simulation; steel (stainless); transformation temperatures (Ac1 and Ac3)

International Standard Serial Number (ISSN)

1544-1024; 1059-9495

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer; ASM International, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Jan 2026

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