Optimization of responses in electron beam welding of inconel-718 alloy using genetic algorithm approach

Abstract

Inconel 718 alloy is a chromium-titanium-nickelbased austenitic alloy with increased strength and better resistance to carburization and oxidation at increased temperatures. This is an important and efficient alloy material for the power plants of next generation [1]. These alloys can be used in sea water, power plant applications with temperature ranges up to 900ºC. The combination of resistance and the improved mechanical property makes this alloy useful in case of various applications, such as exposure of long-term and higher temperatures in corrosive area. High temperature tubes of heat exchanger in gascooled nuclear reactors and super heater tubes are the other applications of Inconel 718 alloy [2]. In addition, EBW process is a metal fabrication technique in the presence of multi-factors and multi-objectives. *Author for correspondenceThis connection of normal metals, like aluminium, steel, and magnesium of 1mm to 6mm thickness in almost all positions can be done using this process [3]. The weld joint quality highly depends on the parameters of the input process and the control of this input process parameter acts as a basic problem to the manufacturer in obtaining increased weld quality in welded joint [4]. Highly skilled engineers or operators select the parameters depending on trial and error method in order to produce a welded joint with necessary indications, but the selection of parameters using this method was time consuming [5]. The welds are utilized to find, whether they are suitable to meet the all the required specification [6]. Many researchers have studied the effect of mechanical properties of Inconel-718 alloy material and few authors published about fabrication, solidification and characterization of these alloys. It is understood that there is lack of papers on the effect of electron beam welding parameters and with their responses of bead width and depth of penetration with quality optimization.