International Journal of Science, Engineering and TechnologyAuthors: Dr Suresh Arjula, Vankudoth Ravinder
Abstract: This review paper unequivocally highlights the essential role of machine learning models in enhancing weld quality and optimizing welding processes, with a strong focus on the revolutionary applications of ML in electron beam welding. By adopting these innovations, the industry can confidently unlock new levels of efficiency and excellence in welding technology. The formation of brittle Ti–Fe intermetallic compounds (IMCs) and the mismatch in thermal properties make welding titanium and stainless steel difficult. These dissimilar metals can be joined using Electron Beam Welding (EBW), which is done under high vacuum and provides precise control with little contamination. Main objective statement: The objective of this project is to investigate the feasibility of welding titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L) using electron beam welding and different filler materials(ni,cu,v). The study aims to analyze the effect of filler materials o the weld quality ,micro structure, and mechanical properties of the joints in order to identify the most suitable filler for high- strength applications. Main task the main task of this project is to weld titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L)using electron beam welding and different filler materials (nickel,copper, vanadium). The welds will be analyzed for microstructure and mechanical properties to determine the most suitable filler material for producing strong and defect-free joints. filler materials (nickel, copper, and vanadium) to fuse titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L). In order to identify the best filler material for creating robust and flawless joints, the microstructure and mechanical characteristics of the welds will be examined.
