ANN Based Predictive Modelling of Weld Shape and Dimensions in Laser Welding of Galvanized Steel in Butt Joint Configurations

Abstract

The quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser welding. Accurate and efficient model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network based model for weld bead geometry prediction and control in laser welding of galvanized steel in butt joint configurations. The proposed approach examines laser welding parameters and conditions known to have an influence on geometric characteristics of the welds and builds a weld quality prediction model step by step. The modelling procedure begins by examining, through structured experimental investigations and exhaustive 3D modelling and simulation efforts, the direct and the interaction effects of laser welding parameters such as laser power, welding speed, fibre diameter and gap, on the weld bead geometry (i.e. depth of penetration and bead width). Using these results and various statistical tools, various neural network based prediction models are developed and evaluated. The results demonstrate that the proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of weld bead geometry under variable welding conditions.

Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures

“…Also, it can instantly generate multiple predictive bead images in a few seconds from the given input laser process conditions once training ends, so is very handy as well as practical (one can also share the trained model online, using the open source deep learning libraries such as TensorFlow and PyTorch on GitHub). Note that several approaches [14]- [16] using an artificial neural network (ANN) have been reported in bead shape prediction in laser welding, however, to the best of the authors' knowledge, no deep learning model has been reported yet.…”

A Study on Prediction of Weld Geometry in Laser Overlap Welding of Low Carbon Galvanized Steel Using ANN-Based Models

“…The Multi-layer feedforward ANN is the most popular and widely used ANN in many applications, especially forecasting, because of their great ability to map nonlinear and complex relationships in multi-inputs multi-output context [11] [12] [13]. Jacques et al proposed an ANN based predictive modelling approach for weld shape and dimensions in butt joint laser welding of galvanized steel [14]. The resulting model presents excellent predictions with an average error less than 10%.…”