Intelligent welding system technologies: State-of-the-art review and perspectives

By Baicun Wang, S. Hu, Lei Sun, Theodor Freiheit. 2020

351 citations recorded by the source corpus.

Abstract Welding systems are being transformed by the advent of modern information technologies such as the internet of things, big data, artificial intelligence, cloud computing, and intelligent manufacturing. Intelligent welding systems (IWS), making use of these technologies, are drawing attention from academic and industrial communities. Intelligent welding is the use of computers to mimic, strengthen, and/or replace human operators in sensing, learning, decision-making, monitoring and control, etc. This is accomplished by integrating the advantages of humans and physical systems into intelligent cyber systems. While intelligent welding has found pilot applications in industry, a systematic analysis of its components, applications, and future directions will help provide a unified definition of intelligent welding systems. This paper examines fundamental components and techniques necessary to make welding systems intelligent, including sensing and signal processing, feature extraction and selection, modeling, decision-making, and learning. Emerging technologies and their application potential to IWS will also be surveyed, including Industry 4.0, cyber-physical system (CPS), digital twins, etc. Typical applications in IWS will be surveyed, including weld design, task sequencing, robot path planning, robot programming, process monitoring and diagnosis, prediction, process control, quality inspection and assessment, human-robot collaboration, and virtual welding. Finally, conclusions and suggestions for future development will be proposed. This review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they modernize their traditional welding stations, systems, and factories.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c3f708bbcf110c8f26c57bb47be0257a75efa69b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Huijun Li
2. Zengxi Pan

In Mechanical Engineering

1. Zengxi Pan
2. Huijun Li
3. Donghong Ding
4. Dominic Cuiuri
5. Volker Wesling
6. Kai Treutler
7. D. Jafari
8. Ian Gibson

Citations in the corpus

• A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system (2020, methodology): “In traditional welding and AM area, different monitoring method has been tried to improve those issues [14].”