Machine Learning based and Experimental Study on Effect of Powder Size Particle on Stainless Steel Microwave Processed Joint
Abstract
This paper examined the effects of powder grain size particle on the joint characterization of SS304 joined by microwave radiation. Three joints were made using three distinct grain sizes of nickel powder (5 µm, 10 µm, and 20 µm). FESEM analysis, EDS analysis, Vickers micro hardness test, tensile test, and surface temperature were used to characterize the joint region. In addition, Support Vector Machine, SVM, based machine learning (ML) model was employed to analyze the relationship between nickel powder particle size and joint properties. An FESEM and EDS examination revealed a joint area rich in nickel phases and carbides. It also demonstrated that as the nickel particle size was reduced, the homogeneity of the joint increased. The model demonstrated strong predictive capability for joint hardness and tensile strength, confirming the effectiveness of data driven approaches in microwave joining characterization. Hardness studies verified that the joint region hardness was enhanced as the nickel powder grain size was reduced. The tensile findings reveal that the smaller the grain size, the better the joint's tensile strength. Higher surface temperatures of joints were observed for smaller powder grain sizes.References
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