Hybrid Optimization Strategy for Evaluating Sustainable Performance of the Electron Beam Welding Process

With the escalation of climate change, the practice of sustainable welding has become imperative in today's manufacturing industries. Sustainable welding practice via advanced modeling and optimization techniques is a recent idea that has highly been emphasized in various studies for the sustainable production of components. This work, therefore, assess the sustainable perfor-mance of the electron beam welding (EBW) process using a novel hybrid optimization search strategy (FL-AHP-TLBO) that optimizes two sustainable characteristics i.e. net energy (Enet) as 'environmental consideration' and depth of penetration (P), weldment area (WA), ultimate tensile strength (UTS) being three quality factors as 'economic consideration'. The optimization approach combines soft computing fuzzy logic (FL) modelling with the teaching-learning-based optimization (TLBO) algorithm to simultaneously optimize these factors while an analytical hi-erarchy process (AHP) is used to determine the optimal weightage for each factor. The approach yields a minimum fitness value of -0.39945 at optimum parameter setting V= 60 kV, I= 38 mA, S= 1200 mm/min, and O= 600 Hz. On validation, the methodology improves the sustainable measures by 1.115% for P, 2.5150% for WA, 7.0423% for Enet, and 2.5836% for UTS. The pro-posed methodology also was found simple and adequate with a faster convergence rate, higher consistency, and results in an overall improvement of 7.377% over the traditional optimization approaches.