A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

dc.contributor.authorPrabhakar, D.A.P.
dc.contributor.authorKorgal, A.
dc.contributor.authorShettigar, A.K.
dc.contributor.authorHerbert, M.A.
dc.contributor.authorGowdru Chandrashekarappa, M.P.G.
dc.contributor.authorPimenov, D.Y.
dc.contributor.authorGiasin, K.
dc.date.accessioned2026-02-08T18:38:35Z
dc.date.issued2023
dc.description.abstractThis 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.
dc.identifier.citationJournal of Manufacturing and Materials Processing, 2023, Vol.7, 5, p. -
dc.identifier.urihttps://doi.org/10.3390/jmmp7050181
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/34246
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.subjectartificial neural network
dc.subjectfriction stir welding
dc.subjectmachine learning
dc.subjectoptimization
dc.subjectprocess monitoring
dc.subjectprocess parameters
dc.subjectresponse surface methodology
dc.subjectTaguchi orthogonal array (OA)
dc.titleA Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

Files