Artificial Intelligence-Empowered Optimization of Industrial Welding Robots

Abstract

This paper provides a comprehensive review of the use of artificial intelligence in the optimization of industrial welding robots, with the goal of addressing major challenges that traditional welding robots face in complex scenarios, such as insufficient robustness, low defect detection efficiency, and difficulties with dynamic parameter adaptation. The research explores how AI utilizes deep learning, machine vision, digital twins, and adaptive control technologies to improve system integration, defect detection, and autonomous learning in welding robots. This document examines the utilization of an enhanced ResNet50 model for the advancement of dynamic tracking and high-precision localization of weld seams, alongside the notable enhancements realized by the lightweight YOLO-DEFW model integrated with data augmentation methods for real-time, efficient, and accurate detection of welding defects. This paper examines the application of artificial neural networks (ANN) and the Fuzzy ARTMAP framework in developing an autonomous learning and dynamic optimization system for welding parameters, thereby effectively mitigating the overdependence on expert experience inherent in traditional methods. The profound integration of AI technology may markedly improve the intelligence, welding quality, and production efficiency of industrial welding robots, offering unique technical avenues and extensive development opportunities for smart manufacturing.