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Additive manufacturing (AM) of multi-principal element alloys (MPEAs) hasgained significant attention in recent years. However, the intricate nature ofphenomena such as rapid solidification, heat gradients, and residual stressespresents challenges in controlling the properties of printed components. Toovercome these challenges, this study utilized machine learning (ML) approachto investigate the correlations between composition, processing parameters,testing conditions, and yield strength of single-phase MPEAs within theCoCrFeMnNi system, produced via laser-melt deposition and laser powder-bedfusion. Multiple algorithms, including Random Forest, Gradient Boosting, andExtreme Gradient Boosting, were trained, and tested. SHapley AdditiveexPlanations algorithm was employed to analyze the contributions of inputfeatures. All models exhibited reasonable accuracy, with Random Forestperforming the best. The impact of data sparsity was examined, and minimalsensitivity to data splitting was observed. Notably, the research yieldedvaluable insights into the key features influencing the yield strength ofMPEAs, showcasing the potential of ML in accurately modeling the materialproperties of additively manufactured components.

Data-driven machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys