Experimental investigations, input-output modeling and optimization for electron beam welding of Cu-Cr-Zr alloy plates. Issue 1 (July 2016)

• Record Type: Journal Article
• Authors: Kanigalpula, P.; Pratihar, D.; Jha, M.; Derose, J.; Bapat, A.; Pal, A.
• Is Part Of: International journal of advanced manufacturing technology. Volume 85:Issue 1/4(2016)
• Page Start: 711
• Page End: 726
• Publication Date: 2016-07
• Subjects: Electron beam welding -- Cu-Cr-Zr alloy -- Optimization; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-015-7964-7
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract Precipitation-hardened Cu-0.804Cr-0.063Zr alloy is a potential material to be used for the fabrication of heat sinks in International Thermonuclear Experimental Reactor (ITER) applications. The softening temperature of these alloys is 500 °C, which makes it suitable for the fabrication of components subjected to high heat flux. During welding, the precipitates get completely dissolved in the melted zone, which results in lower hardness and softening temperature. The aim of this study was to understand the effects of electron beam welding (EBW) on weld bead geometry and mechanical properties of this alloy. Accelerating voltage, beam current, welding speed, and focusing distance were selected as input variables, and bead geometric parameters like bead width, bead penetration, and microhardness values of the fusion and heat-affected zones were considered as outputs. Statistical regression analysis was conducted to determine input-output relationships. All the above input parameters were found to have significant contributions toward bead penetration and microhardness of fusion zone. However, accelerating voltage did not have significant contribution toward bead width. Optimization was done to obtain minimum weldment area after satisfying the conditions of maximum bead penetration and minimum deviation of the hardness of fusion zone from that of parent material. This constrained optimization problem was solved using a genetic algorithm along with a penalty function approach for handling the functional constraints. A good agreement was obtained between the results predicted by the optimization tool and that of real experiments. Thus, the genetic algorithm (GA) could accurately establish input-output relationships of this process.