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.
Highlights
IntroductionAs the process parameters are interrelated by nonlinear relationships, this procedure cannot lead to convincing results despite the prohibitive number of experiments, which leads to excessive time and costs
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 results provide less than 10% error and shows that the mixture of finite element method (FEM) and artificial neural network (ANN) can be used to predict welds shape accurately
Summary
As the process parameters are interrelated by nonlinear relationships, this procedure cannot lead to convincing results despite the prohibitive number of experiments, which leads to excessive time and costs These problems can be avoided if appropriate prediction model is designed. The objective of this paper is to present a structured and comprehensive approach developed to design an effective ANN based model for predicting weld shape and dimensions (WSD) in LW of galvanized steel in butt joint configurations using a 3 kW Nd:Yag LW system. Elling procedure is based on a structured experimental investigation and exhaustive 3D FEM simulation efforts in order to identify the possible relationships between LW parameters (laser power, welding speed, fibre diameter and gap) and the weld geometrical characteristics such as depth of penetration (DOP) and bead width (BDW), and the sensitivity of these relationships to the welding process conditions. In order to carry out the models building procedure, an efficient modelling planning method combining neural networks, a multi-criteria assessment and various statistical analysis tools is adopted
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