Prabhakar, D.A.P.Korgal, A.Shettigar, A.K.Herbert, M.A.Gowdru Chandrashekarappa, M.P.G.Pimenov, D.Y.Giasin, K.2026-02-052023Journal of Manufacturing and Materials Processing, 2023, Vol.7, 5, p. -https://doi.org/10.3390/jmmp7050181https://idr.nitk.ac.in/handle/123456789/28273This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed. © 2023 by the authors.artificial neural networkfriction stir weldingmachine learningoptimizationprocess monitoringprocess parametersresponse surface methodologyTaguchi orthogonal array (OA)