Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

Abstract

Nickel-based superalloys are widely used in fabrication of components in aero space and nuclear sectors due to excellent strength, corrosion resistance, good ductility and high-temperature resistance. Inconel 825 superalloy is predominantly used for making aircraft engine components. This work investigates single pass welding of Inconel 825 strips employing gas tungsten arc welding. Four weld parameters, viz. welding speed (V), welding current (I), arc length (N) and gas flow rate (GFR), were used to investigate ultimate tensile strength (UTS) of weld employing Box–Behnken design having 27 experiments. The welding current is found the most dominating factor followed by welding speed (V). Higher heat input with low welding speed increases deposition rate that ensures more strength in weldment. Microstructural analysis shows two different grain boundaries, i.e., solidification sub-grain boundaries and solidification grain boundaries, in fusion zone. Artificial neural network (ANN) modelling for UTS is developed, and its predictive capability is compared with multiple regression analysis. A new integrated ANN–TLBO-based soft computing modelling and optimization approach is proposed for obtaining optimum weld parameters to maximize weld strength. The proposed optimization methodology obtained maximum UTS of 701.73 MPa at optimal weld parameters I = 120 A, V = 180 mm/min, GFR= 12 l/min and N = 2.24 mm. Validation result was found highly encouraging with 0.60% error with confirmation experiment. The proposed method has better convergent capability with minimum number of iterations.