...Katika Harikrishna; Abeyram Nithin; M. J. Davidson In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs various supervised ML...

...M. Shafiqur Rahman; Naw Safrin Sattar; Radif Uddin Ahmed; Jonathan Ciaccio; Uttam K. Chakravarty This study presents a cost-effective and high-precision machine learning (ML) method for predicting the melt-pool geometry and optimizing the process parameters in the laser powder-bed fusion (LPBF...

...Pau Cutrina Vilalta; Somayyeh Sheikholeslami; Katerine Saleme Ruiz; Xin C. Yee; Marisol Koslowski We applied machine learning models to predict the relationship between the yield stress and the stacking fault energies landscape in high entropy alloys. The data for learning in this work were taken...