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Additive friction stir deposition (AFSD) is a novel solid-state additivemanufacturing technique that circumvents issues of porosity, cracking, andproperties anisotropy that plague traditional powder bed fusion and directedenergy deposition approaches. However, correlations between process parameters,thermal profiles, and resulting microstructure in AFSD remain poorlyunderstood. This hinders process optimization for properties. This work employsa framework combining supervised machine learning (SML) and physics-informedneural networks (PINNs) to predict peak temperature distribution in AFSD fromprocess parameters. Eight regression algorithms were implemented for SMLmodeling, while four PINNs leveraged governing equations for transport, wavepropagation, heat transfer, and quantum mechanics. Across multiple statisticalmeasures, ensemble techniques like gradient boosting proved superior for SML,with lowest MSE of 165.78. The integrated ML approach was also applied toclassify deposition quality from process factors, with logistic regressiondelivering robust accuracy. By fusing data-driven learning and fundamentalphysics, this dual methodology provides comprehensive insights into tailoringmicrostructure through thermal management in AFSD. The work demonstrates thepower of bridging statistical and physics-based modeling for elucidating AMprocess-property relationships.

Supervised Machine Learning and Physics based Machine Learning approach  for prediction of peak temperature distribution in Additive Friction Stir  Deposition of Aluminium Alloy