Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys

Subedi, Upadesh; Coutinho, Yuri Amorim; Malla, Prafulla Bahadur; Gyanwali, Khem; Kunwar, Anil

doi:10.3390/met12060964

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…ANN-based models have found a wide application in metallic materials science for the last time [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. U. Subedi et al have determined the presence of an intermetallic phase in multi-principal element alloys by ANN modeling [ 30 ]. X. Geng et al have predicted the hardenability of non-boron steels by machine learning [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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The development of new lightweight materials is required for the automotive industry to reduce the impact of carbon dioxide emissions on the environment. The lightweight, high-manganese steels are the prospective alloys for this purpose. Hot deformation is one of the stages of the production of steel. Hot deformation behavior is mainly determined by chemical composition and thermomechanical parameters. In the paper, an artificial neural network (ANN) model with high accuracy was constructed to describe the high Mn steel deformation behavior in dependence on the concentration of the alloying elements (C, Mn, Si, and Al), the deformation temperature, the strain rate, and the strain. The approval compression tests of the Fe–28Mn–8Al–1C were made at temperatures of 900–1150 °C and strain rates of 0.1–10 s−1 with an application of the Gleeble 3800 thermomechanical simulator. The ANN-based model showed high accuracy, and the low average relative error of calculation for both training (5.4%) and verification (7.5%) datasets supports the high accuracy of the built model. The hot deformation effective activation energy values for predicted (401 ± 5 kJ/mol) and experimental data (385 ± 22 kJ/mol) are in satisfactory accordance, which allows applying the model for the hot deformation analysis of the high-Mn steels with different concentrations of the main alloying elements.

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Section: Introductionmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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“…Machine learning assisted material synthesis is one of such tools which is at the forefront of the research and development, superseding traditional empirical methodologies with computational data driven approach. Numerous material databases [15], tools [16] and applications [17] have been developed in recent times, promoting, enhancing and speeding up the quantification and design of newer and superior materials efficiently and effectively.…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning

Subedi

Poudel

Gyanwali

et al. 2022

Metals

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Though the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing machine learning (ML) techniques for the study of the martensitic alloys, and such data-driven approaches are expected to unravel a great amount of information about the process-structure-property behaviour relationship in this class of materials. However, with the availability of a large variety of datasets and with an option to use different ML models, a bulk amount of information has already been generated with regard to martensitic alloys. The discovery and design of shape memory alloys can be accelerated if the multi-principal element functional alloys and martensitic transformation phenomenon are studied extensively using machine learning techniques. Thus, it is necessary to highlight the major categories or aspects of these alloys that have been predicted with ML. The present work performs a state-of-the-art review on the machine learning models developed for the quantification of aspects such as martensitic start temperature (Ms), materials properties, microstructure, mechanisms etc., on the alloys.

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Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach

Chen,

Han,

Zhang

et al. 2024

Computational Materials Science

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Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys

Cited by 4 publications

References 27 publications

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning

Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach

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