Prediction of internal welding penetration based on IR thermal image supported by machine vision and ANN-model during automatic robot welding process

Abstract

Welding quality is a critical criterion for evaluating welding operations. Traditional evaluation methods suffer from drawbacks such as lack of objectivity, untimeliness, and high costs. Therefore, real-time monitoring and assessment of the weld pool have become the mainstream trend in welding technology. This study introduces a novel method for defining weld pool width boundaries. It utilizes an infrared (IR) camera to capture the weld pool temperature clusters and employs the Sobel operator for convolution to generate the gradient map of the weld pool temperature clusters. Through enhanced processing in the gradient map, the width boundaries of the weld pool are more effectively detected compared to previous methods. Previous studies defined weld pool width boundaries by identifying characteristic points with the most distinct temperature fluctuations, caused by the different radiative properties of the same material in different states. However, practical tests revealed susceptibility to interference from reflected arc light. The proposed method mitigates the impact of reflected arc light and is applicable to complex multilayer welding scenarios. To address the lag in quality monitoring, reduce welding costs, and achieve real-time monitoring of the weld pool process, we employed machine vision and an artificial neural network (ANN) model. This led to the development of a weld penetration assessment system based on infrared thermal images. The system successfully predicted the penetration depth for 4 mm carbon steel with an accuracy of 86.6 %. This validates the feasibility of estimating and predicting weld performance using the surface temperature characteristics of the weld pool. The newly proposed weld pool boundary definition method holds promise for real-time monitoring in more complex multilayer pipe welding scenarios. It lays the groundwork for predicting and fusing the weld depth in intricate multi-pass pipe welding. © 2024 The Author(s)