Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review

Publication Name

Robotics and Computer-Integrated Manufacturing

Abstract

Recent development in the Wire arc additive manufacturing (WAAM) provides a promising alternative for fabricating high value-added medium to large metal components for many industries such as aerospace and maritime industry. However, challenges stemming from the demand for increasingly complex and high-quality products, hinder the widespread adoption of the conventional WAAM method for manufacturing industries. The development of artificial intelligence (AI) techniques may provide new opportunities to upgrade WAAM to the next generation. Hence, this paper provides a comprehensive review of the state-of-the-art research on AI techniques in WAAM. Firstly, we proposed a novel concept of intelligent wire arc additive manufacturing (IWAAM) and revealed the challenges of developing IWAAM. Secondly, an overview of the research progress of applying AI techniques to several aspects of the WAAM process chain, including fabrication process pre-design, online deposition control and offline parameter optimization is provided. Thirdly, the relevant machine learning algorithms, and the knowledge of corresponding AI techniques, are also reviewed in detail. Through reviewing the current research articles, issues of applying AI techniques to the WAAM process are presented and analysed. Finally, future research perspectives in terms of novel AI technique applications and AI technique enhancement are discussed. Through this systematic review, it is expected that WAAM may gradually develop into a smart/intelligent manufacturing technology in the context of Industry 4.0 through the adoption of AI techniques.

Open Access Status

This publication is not available as open access

Volume

82

Article Number

102525

Funding Number

202008200004

Funding Sponsor

China Scholarship Council

Share

COinS
 

Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.rcim.2023.102525