Process Parameter Sensitivity in Magnetic Pulse Welding: An Artificial Neural Network approach
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Date
2021-10-14
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Abstract
Magnetic pulse welding (MPW), a solid-state impact welding technique provides the ability
to join a wide array of material combinations, whilst introducing little to no heat to the
system and preserving the base metal microstructure. Impact velocity is one of the key
criteria which determines the weldability of the joint during MPW. Experimental
measurement of impact velocity in MPW across wide-ranging parameters is expensive and
time-consuming. Therefore, guidelines for process selection and knowledge of relative
influence of parameters on impact velocity is limited. This study presents the applicability
of coupling finite element method (FEM) and artificial neural network (ANN) modelling to
perform sensitivity analysis of MPW. The welding process was simulated using FEM, and
multilayer modular feedforward networks based on the results from finite element
simulations were developed. The results of the present study revealed that the coil cross sectional area and turns primarily governed the process, followed by the voltage. The
relative sensitivity of the parameters remained independent of the material combination.
Inclusion of shop floor applicable process parameters suggests that the developed ANN
models can substantially narrow down experimental runs and simultaneously act as a
decision support tool for end users.
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Keywords
magnetic pulse welding, sensitivity analysis, finite element method, artificial neural network, impact velocity