Towards Digital Twin–Enabled Laser Welding: Opportunities, Challenges, and a Hybrid Framework for Industrial Deployment

Hadhami Benslama; Sami Chatti; Mahmood Mohammed Hamzah

doi:10.51173/jt.v8i2.2950

Authors

Hadhami Benslama Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
Sami Chatti Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
Mahmood Mohammed Hamzah Faculty of Technology, Ferhat Abbas University 1, Setif, Algeria

DOI:

https://doi.org/10.51173/jt.v8i2.2950

Keywords:

Laser Welding (LW), Digital Twin (DT), Artificial Intelligence (AI), CNN-LSTM, Multi-Sensor Integration, Smart Manufacturing, Real-Time Process Control

Abstract

The recent advancements in laser welding (LW) technology through the incorporation of Digital Twin (DT) technologies, due to the growing importance of Industry 4.0 and smart manufacturing in the context of automation, precision, and digital fabrication, have made LW one of the world's leading advanced production processes. The DT technologies are expected to provide the necessary integration to facilitate effective real-time monitoring, predictive modeling of process behavior, optimal process control, and adaptive process control capability for welded parts. However, an overall framework for effectively integrating LW technology into the manufacturing environment has yet to be developed. This paper critically reviews recent developments in DT-enabled LW systems and proposes a hybrid framework for successful industrial deployment. For data collection, the authors used a systematic review approach to identify studies published after 2018 that covered various technologies related to DTs applied to welding. The review included consideration of real-time sensing technologies; data fusion using multi-sensor technologies; physically based modeling technologies; artificial intelligence technologies; EDGE-Cloud Computing architectures; and adaptive process control strategies. The findings indicate that developing a physics-informed simulation of the welding process and integrating it with an AI-based prediction model will significantly improve accuracy in assessing weld quality, predicting defect occurrence, and optimizing the process. The integration of EDGE Computing and Cloud Computing with Office & Manufacturing processes supports real-time responses and scalability within the industry. Some of the major challenges identified in the research included data synchronization, interoperability, cybersecurity, computational latency, and explainable artificial intelligence. A hybrid DT framework is proposed to provide an intelligent, autonomous platform for future LW applications in Smart Manufacturing.

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Author Biographies

Hadhami Benslama, Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia

Sami Chatti, Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia

Mahmood Mohammed Hamzah, Faculty of Technology, Ferhat Abbas University 1, Setif, Algeria

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