A Rapid, Open-Source CCT Predictor for Low-Alloy Steels, and Its Application to Compositionally Heterogeneous Material

Collins, J.F.; Piemonte, Martina; Taylor, Mark D.; Fellowes, Jonathan; Pickering, Ed

doi:10.3390/met13071168

Cited by 5 publications

(2 citation statements)

References 60 publications

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“…The results of the calculations consisted of the temperatures at the beginning and end of a particular transformation, the volume fraction of structural components, and the hardness of steel after heat treatment. Recently, Collins et al [13] proposed a model for predicting the CCT behaviour of low-alloy steels using semi-empirical expressions for isothermal transformation. Collins et al still cite that some of the more popular software packages include JMatPro, Thermo-Calc, and MAP_STEEL_MUCG83.…”

Section: Introductionmentioning

confidence: 99%

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Proposal and Assessment of a Multiple Cycle-Continuous Cooling Transformation (MC-CCT) Diagram for Wire Arc Additive Manufacturing of Thin Walls

Högström,

Fadaei,

Rahimi

et al. 2023

Metals

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Continuous cooling transformation (CCT) diagrams of base metals are common in welding. They can be built using physical or numerical simulations, each with advantages and limitations. However, those are not usual for weld metal, considering its variable composition due to the dilution of the weld into the base metal. Wire Arc Additive Manufacturing (WAAM) is a distinctive case in which the interest in materials comparable with weld composition raises attention to estimating their mechanical properties. Notwithstanding, this concept is still not used in WAAM. Therefore, the aim of this work was to address a methodology to raise MC-CCT (Multiple Cycle Continuous Cooling Transformation) diagrams for WAAM by combining physical and numerical simulations. A high-strength low-alloy steel (HSLA) feedstock (a combination of a wire and a shielding gas) was used as a case study. To keep CCT as representative as possible, the typical multiple thermal cycles for additive manufacturing thin walls were determined and replicated in physical simulations (Gleeble dilatometry). The start and end transformations were determined by the differential linear variation approach for each thermal cycle. Microstructure analyses and hardness were used to characterise the product after the multiple cycles. The same CCT diagram was raised by a commercial numerical simulation package to determine the shape of the transformation curves. A range of austenitic grain sizes was scanned for the curve position matching the experimental results. Combining the experimental data and numerically simulated curves made estimating the final CCT diagram possible.

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Section: Introductionmentioning

confidence: 99%

“…13, 1533 15 of 21 the hardness drop suggest the Bainite transformation was tempered by the 4th cycle (below any transformation during reheating). Therefore, the numerical simulation meets evidence in the experimental work that it represents the phenomenon of multiple cycles over the material.…”

mentioning

confidence: 99%

Proposal and Assessment of a Multiple Cycle-Continuous Cooling Transformation (MC-CCT) Diagram for Wire Arc Additive Manufacturing of Thin Walls

Högström,

Fadaei,

Rahimi

et al. 2023

Metals

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Prediction of Time–Temperature–Transformation Diagrams of NiAl Alloy: An Evaluation of Intelligent Algorithms

Hernández‐Flores,

Talavera‐Rivera,

García‐Moreno

2024

Adv Eng Mater

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Time‐temperature‐transformation diagrams are essential in the field of metallurgy. However, constructing these diagrams through empirical and simulating methods can be time‐consuming and expensive. Therefore, there is a need to develop accurate and affordable alternatives that can rapidly predict TTT diagrams. The present study offers a novel analysis of multiple algorithms for predicting TTT diagrams, as well as an examination of various data preprocessing techniques (data augmentation ‐ DA and exploratory data analysis ‐ EDA). A database is created by integrating multiple data sources and increased using DA by ensuring the synthetic data remains physically realistic and meaningful. Subsequently, an EDA is performed to prepare the data before its use in training. The influence of each hyperparameter on the prediction was studied and optimal hyperparameter configuration was defined for each algorithm. The performance of the algorithms is evaluated using RMSLE, RMSE, R2, Mean IoU, and SMAPE. The multilayer perceptron demonstrated the greatest robustness in predicting TTT curves. Additionally, the Ft‐Transformer is a viable alternative if an appropriately sized dataset is available. These results provide valuable insights into the use of Machine Learning techniques as a new alternative in predicting isothermal transformation curves.

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Modeling Continuous Cooling Transformations for HSLA Steels With Physical Metallurgy Guided Hereditary Machine Learning

Cao,

Cui

et al. 2023

Metall Mater Trans A

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A Rapid, Open-Source CCT Predictor for Low-Alloy Steels, and Its Application to Compositionally Heterogeneous Material

Cited by 5 publications

References 60 publications

Proposal and Assessment of a Multiple Cycle-Continuous Cooling Transformation (MC-CCT) Diagram for Wire Arc Additive Manufacturing of Thin Walls

Proposal and Assessment of a Multiple Cycle-Continuous Cooling Transformation (MC-CCT) Diagram for Wire Arc Additive Manufacturing of Thin Walls

Prediction of Time–Temperature–Transformation Diagrams of NiAl Alloy: An Evaluation of Intelligent Algorithms

Modeling Continuous Cooling Transformations for HSLA Steels With Physical Metallurgy Guided Hereditary Machine Learning

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