A review on optimization of autonomous welding parameters for robotics applications

Abstract

In order to withstand the competitive nature of the industrial market and maintain the longevity of products, researchers try to enhance current technologies and create cost-effective solutions. Aside from acquiring new machinery, it also involves successfully managing actual process variables. To get the desired and financially advantageous results, it is necessary to measure, control, and optimize these process variables. The welding process is significantly influenced by its characteristics, which play a major role in assessing the weld quality and reducing the welding time while ensuring the elimination of defects. This study provides a comprehensive overview of the research findings, developments, and remarkable techniques. First, the effective old-trade techniques applied for welding optimization are discussed. Then, the sophisticated methods depending on AI are handled for adaptive welding control, such as ANN in tandem with GA models, ant colony optimization technique, and the NSGA-III algorithm. After that, summarize the relevant research related to building models with supportive vision sensing elements for seam tracking, monitoring the weld pool, and handling feedback control. Finally, the future research difficulties and directions toward real-time intelligent monitoring are highlighted. This review will help aspiring and ambitious researchers gain a comprehensive understanding of welding optimization for robotics applications.