Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction

Abstract

The friction stir welding (FSW) method was used to weld B4C reinforced AA 5083 metal matrix composites in this study. By coating titanium nitride (TiN), aluminium chromium nitride (AlCrN), and diamond-like carbon (DLC) to a thickness of 4 microns, three FSW tools with square pin profiles were developed and the friction coefficients of 0.69, 0.32, and 0.2 were maintained. At three levels, the process factors such as tool rotating speed, transverse feed, and axial force were examined. For each tool, 15 samples were made using the central composite design. The influence of the friction coefficient on ultimate tensile strength, microstructural features, and tool condition was studied, and the flower pollination algorithm (FPA) technique was used to find the best process parameters for obtaining maximum ultimate tensile strength of FSW joints. The improved tensile strength of FSW joints was verified using a validation test. The coating has a considerable influence on the ultimate tensile strength, microstructure, and tool condition, according to the results of the tool’s friction coefficient. The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective.