High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning

Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with highaccuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveragingthe Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic datato simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, FractureStress, and Young’s Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generatinga dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96.61% accuracy rate inforecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model’s reliability,predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model’s predictions.The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable sourcefor training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS)and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements inprediction speed and accuracy.