Machine Intelligence in Welding Process

Dear Colleagues,

Rapid advancements in machine intelligence, particularly in the realms of artificial intelligence, machine learning, surrogate modeling, computer vision, and robotics, have significantly impacted various industries. One such industry undergoing a transformative phase is welding, a fundamental process in manufacturing and fabrication.

This Special Issue aims to explore the synergies between machine intelligence and welding processes, encompassing a wide spectrum of research and applications.

The topics of interest for this Special Issue include the following:

1. Intelligent Welding Automation: Integration of artificial intelligence (AI) for autonomous or semi-autonomous welding processes for improved process efficiency and reduced human intervention.
2. Machine Learning (ML) in Fabrication: Implementation of ML algorithms for pattern recognition, defect detection, and adaptive control in welding processes, achieving higher precision and quality.
3. Computer Vision in Weld Inspection: Utilization of computer vision techniques, image processing, and deep learning for real-time monitoring, defect detection, and quality assessment in welding.
4. Digital Twins for Welding: Integration of digital twin technology for real-time monitoring, predictive maintenance, and performance optimization.
5. Innovative Applications of Machine Intelligence: Case studies and advancements in various industries, showcasing the transformative potential of machine intelligence.
6. Augmented Reality (AR) in Welding: Exploration of AR technologies for providing real-time feedback, guidance, and training support to welders, enhancing their skills and efficiency.
7. Industry 5.0 Integration: Examination of machine intelligence integration within the context of Industry 5.0, emphasizing human–machine collaboration, customization, and sustainability.
8. Additive Manufacturing (AM) and Welding: Potential integration of manufacturing solutions applied in AM to welding, highlighting opportunities for expanded capabilities.
9. Surrogate Modeling for Structural Integrity: Development of meta-models and ML techniques to link process parameters to key outcomes for structural integrity and in-service performance.
10. Modeling and Simulation: Emphasis on using validated models as an integral component of predictive data-centric methodologies.
11. Challenges, Future Directions, and Emerging Trends: Discussions on the prospects of the integration of machine intelligence in welding processes, providing insights into future directions and opportunities.

Dr. Anastasia Vasileiou
Guest Editor

Manuscript Submission Information

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• machine intelligence
• welding
• Industry 5.0
• additive manufacturing
• manufacturing process