In Situ Tem Observations of the Growth of Bainitic Ferrite in an Fe-0.3c-3Mn-1.5Si-0.15Mo Steel

Nutter, John; Qi, Jiahui; Farahani, Hussein; Rainforth, Mark W.; Zwaag, Sybrand van der

doi:10.2139/ssrn.4272556

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…Numerous methodologies, such as thermodynamics-based methods, linear regression, artificial neural network (ANN) modeling and machine learning, have been applied to predict the martensite transformation start (MS) temperature [ 14 , 15 , 16 ]. In the meantime, due to the virtues of bainitic steels, such as high strength, ductility, toughness and creep resistance at reasonable costs, the austenite-to-bainite transformation has also gained lots of interest [ 17 ]. Ac1 and Ac3 temperatures are the main parameters used to design the heat treatment process of steels.…”

Section: Introductionmentioning

confidence: 99%

Phase Transformation Temperature Prediction in Steels via Machine Learning

Zhang,

Cheng,

Pan

et al. 2024

Materials

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The phase transformation temperature plays an important role in the design, production and heat treatment process of steels. In the present work, an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation temperatures, namely Ac1, Ac3, the martensite transformation start (MS) temperature and the bainitic transformation start (BS) temperature. The effects of the alloying element were discussed in detail by comparing their influencing mechanisms on different phase transformation temperatures. The training accuracy was significantly improved by further introducing appropriate features related to atomic parameters. The melting temperature and coefficient of linear thermal expansion of the pure metals corresponding to the alloying elements, atomic Waber–Cromer pseudopotential radii and valence electron number were the top four among the eighteen atomic parameters used to improve the trained model performance. The training and prediction processes were analyzed using a partial dependence plot (PDP) and Shapley additive explanation (SHAP) methods to reveal the relationships between the features and phase transformation temperature.

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Section: Introductionmentioning

confidence: 99%

Phase Transformation Temperature Prediction in Steels via Machine Learning

Zhang,

Cheng,

Pan

et al. 2024

Materials

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“…Ma and co-workers [31] used a novel heating system with multi-layer thermal insulation to prevent the effect of thermal electrons which allowed to capture a high quality image at elevated temperature. In a recent study, Nutter et al [32] studied bainite sheaf growth using in-situ TEM.…”

Section: Introductionmentioning

confidence: 99%

In-Situ Sem Characterization and Numerical Modelling of Bainite Formation and Impingement of a Medium-Carbon, Low-Alloy Steel

Seppälä¹,

Pohjonen²,

Mendonça³

et al. 2023

Preprint

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Advances in Heat Treatment and Microstructural Optimization of Hadfield Steel for Enhanced Wear Resistance

Pinto Junior,

Teixeira,

Silva

2024

RGSA

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Objective: This study aims to explore the impact of heat treatment processes on carbide formation in Hadfield steel, focusing on optimizing its microstructure and mechanical properties for industrial applications that require high wear resistance. Theoretical Framework: The research is grounded in theories of metallurgical transformation and work hardening, particularly in relation to the metastable austenitic structure of Hadfield steel, which transforms into martensite under impact. This transformation mechanism, alongside alloy composition and heat treatment, shapes the steel’s resistance to wear and mechanical strength. Method: A systematic literature review was conducted, encompassing 11 relevant studies on Hadfield steel from four scientific databases: Taylor & Francis, Springer, Wiley, and ScienceDirect. The selected studies were analyzed using the PRISMA methodology to evaluate the influence of heat treatments—such as austenitization, quenching, and tempering—on carbide formation and microstructure. Results and Discussion: Findings reveal that specific heat treatments significantly enhance Hadfield steel’s wear resistance and strength. The influence of processes like austenitization on carbide dissolution and rapid cooling to avoid carbide precipitation has proven critical for the steel’s toughness. This discussion aligns the observed improvements with theoretical predictions and identifies challenges in carbide control for enhanced performance. Research Implications: The study provides practical insights for industries utilizing Hadfield steel in high-wear environments, such as mining and transportation, and proposes further research into innovative heat treatment strategies. Originality/Value: This study contributes novel perspectives on the optimization of Hadfield steel's heat treatment processes, potentially informing advanced manufacturing techniques to improve the steel’s durability and economic value in key industrial applications.

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In Situ Tem Observations of the Growth of Bainitic Ferrite in an Fe-0.3c-3Mn-1.5Si-0.15Mo Steel

Cited by 3 publications

References 47 publications

Phase Transformation Temperature Prediction in Steels via Machine Learning

Phase Transformation Temperature Prediction in Steels via Machine Learning

In-Situ Sem Characterization and Numerical Modelling of Bainite Formation and Impingement of a Medium-Carbon, Low-Alloy Steel

Advances in Heat Treatment and Microstructural Optimization of Hadfield Steel for Enhanced Wear Resistance

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