Optimization of processing parameters of cold metal transfer joined 316L and weld bead profile influenced by temperature distribution based on genetic algorithm

Austenitic stainless steel alloys find the wide range of application in modern industries like pipework, containers, food production and in medical industries for its excellent processing properties and corrosion resistance. There is enormous literature report on the mechanical properties, appropriate joining of materials using different fusion welding processes. Consequently, the cold metal transfer technique appears to weld materials with low heat input which is a noticeable feature of this welding process. In this paper, cold metal transfer welding is performed on austenitic stainless steel material 316L and its bead geometries such as reinforcement height, depth of weld penetration and bead width profile are examined. The temperature distribution at the welding line is observed by means of the data acquisition unit. Genetic algorithm based optimization technique is used to achieve the desired combination of input variables and weld bead geometry. This developed genetic algorithm optimizes the welding process parameters and geometry of the weld bead, by minimizing the least square error based objective function. The investigation outcome of this paper provides an insight into the characterization of the weldment, the effects of weld current and weld travel speed on temperature profile and mechanical properties include hardness, tensile and residual profiles.