Experimental and Theoretical Investigations on Cold Metal Transfer Welds Using Neural Networks: A Computational Model of Weld Geometry

Abstract

Cold metal transfer (CMT) welding is a sophisticated version of fusion welding process available with advanced features incorporated in it. During the process of joining metallic parts with CMT, the bead shape and size have significant effects on the weld quality. Typically, bead geometry is characterized by the three important parameters namely weld width, weld depth and reinforcement height. For rapid and intelligent welding, it is imperative to monitor the bead shape and size during the process. However, the measurement system of three parameters is often time consuming and complex. In this paper, an attempt is made to reduce the computational time of artificial intelligence models that are used for intelligent welding processes. Bead coefficient is modelled as an alternative for bead geometry properties. Computationally efficient artificial neural network models are developed for examining the feasibility of bead coefficient over bead geometry properties with and without online-temperature measurements. The predicted results of both forward and reverse neural network models are in good coherence with the experimental values. The outcome of this study is expected to provide a detailed understanding of effects of process parameters on bead geometry and bead coefficient, which facilitates online monitoring of welding processes.