Nano-scale multi-layered coatings for improved efficiency of ceramic cutting tools

Electron beam welding, though considered a sophisticated welding process, still requires the operator to first carry out several trial welds to find the right combination of welding parameters based on intuition and experience. This archaic method is often unreliable, leading to unproductive manufacturing lead time, man hours, quality control tests, and material wastage. The current study eliminates this “trial and error” method by providing a reliable model which can predict the right combination of weld parameters to achieve a high-quality weld. Beads on plate welds were carried out on AISI 304 stainless steel plates using a low-kilovolt electron beam welding (EBW) machine. A model that can predict weld bead geometry and provide optimized output for minimum weld area condition without compromising on weld quality was developed. Experimental data were collected as per full factorial design of experiments, and the levels for each input parameter were established through pilot experiments. A multivariate regression analysis has been conducted to establish a relationship between four weld input parameters (three levels each) and four weld bead responses. Response surface methodology (RSM) has been used to study the interrelationship between input parameters and their effect on each response variable. Further, minimization of weld cross-sectional area was done using genetic algorithm for maximum penetration and minimum weld area condition. The optimized mathematical model convincingly establishes that the focusing current is a significant input parameter with very high influence over the weld bead geometry. Extensive material characterization and mechanical tests have been carried out to validate the regressed input-output relationship and the optimized mathematical model.