Multi-Criteria Optimization of Friction Stir Welding Process Parameters: A Comparative Research and Review Analysis
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Abstract
Friction stir welding (FSW) is a solid-state joining technology whose performance depends strongly on the coordinated selection of process parameters. Tool rotational speed, traverse speed, material combination, and other controllable variables influence mechanical performance, corrosion behavior, joint quality, and process stability. Because these responses can conflict with one another, conventional single-response optimization is insufficient for many practical FSW applications. This research and review paper develops a comparative analytical framework for understanding how response surface methodology (RSM), regression analysis, multi-criteria decision-making (MCDM), TOPSIS, WASPAS, MORSM, grey relational analysis, and intelligent multi-objective optimization approaches have been applied to FSW parameter selection. Only the ten supplied references are synthesized. The analysis indicates that MCDM-oriented approaches provide a systematic mechanism for converting multiple performance criteria into an integrated decision, while RSM and regression-based methods provide stronger process-response modeling capabilities. TOPSIS and grey relational analysis are particularly useful when several competing quality characteristics must be considered simultaneously, whereas WASPAS and hybrid MCDM approaches extend the decision space through alternative weighting and aggregation mechanisms. The reviewed evidence also indicates a research gap concerning unified optimization architectures capable of connecting process-level FSW optimization with broader lean production and digital manufacturing objectives. The paper therefore positions multi-objective FSW optimization as a decision layer that can potentially support digitally integrated manufacturing environments.
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