Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

Abstract

Multiple-conditioned welding monitoring is a challenging issue in complex robotic welding tasks. In practice, the monitoring system has to be sensitive to different welding conditions (WCs) and weld quality changes. In this paper, a swing high temperature sensor system is used in order to obtain the temperature distribution curve under different WCs. A sigmoid feature extraction (SFE) method is proposed to obtain the geometric features of the temperature distribution curve, and a weld monitoring algorithm is proposed for multiple-conditioned welding tasks using multi-layer perceptron (MLP) classifier and Kalman filter-based Gaussian probability density function (PDF) prediction for the probabilistic weld quality estimation. When there are unknown WCs, the proposed method uses an efficient incremental learning for the MLP and an online maximum likelihood estimation for the Gaussian models of the unknown WCs. The experimental results show that the proposed framework can accurately reflect the weld quality changing in both single-WC and multiple-WC tasks. In addition, the proposed adaptive updating methodology can achieve comparable performance with unknown WCs, as compared to the results with knowing all the WCs.

Welding robot automation technology based on digital twin

“…At the same time, considering the problem of image noise, a method based on attention dense convolutional blocks is adopted to solve the problem of image noise. Finally, the research model will be applied to specific robot welding scenarios, and the technology studied can accurately extract weld texture features, thereby ensuring the quality of welding robots (Xia et al, 2022) Mikkelstrup et al found that in some complex welding robot work scenarios, traditional welding robots are difficult to effectively ensure the quality of welding. On the basis of digital twin technology, this study focuses on high-frequency mechanical impact treatment.…”