Study on laser beam butt-welding of NiTinol sheet and input-output modelling using neural networks trained by metaheuristic algorithms

Though NiTinol, the equiatomic Ni-Ti alloy, is revolutionizing different fields of engineering, manufacturing and medicine since its discovery as a functionally advanced material, poor formability and machinability, and the limitations of available fabrication techniques applied for this alloy are restricting its applications in the form of various desired products. Now-a-days, laser welding is the mostly reported joining technique applied to the alloy. Though the difficulties associated with fabrication method and the cost of the alloy are high enough, a computationally efficient model for quick and precise prediction of operating parameters in order to obtain the specified attributes of the laser welded NiTinol sheets is significantly missing in the literature. In the present work, basic metallographic study related to laser welding of NiTinol sheets in butt-joint configuration was investigated. At the same time, an efficient forward and reverse model had been developed with the help of artificial neural network (ANN). These ANNs were trained with the help of regression equation model using five different metaheuristic techniques separately for the development of both forward and reverse models. Laser power, scan speed, frequency, duty factor and focal positions were treated as the input process variables and three different weld attributes, namely bead-area, micro hardness of the bead and tensile strength of the bead were considered as output variables of the process. The developed model was capable enough of predicting the outputs, which were in good agreement with the experimental data, for both forward and reverse models. The novelty of this study deals with the development and testing of feed-forward neural network and recurrent neural network trained using five different metaheuristic techniques for both the forward and reverse modelling of fibre laser welding of NiTinol alloys.