Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review

Huang, Yi-Wei; Gao, Xiang-Dong; Gao, Perry P.; Ma, Bo; Zhang, Yan-Xi

doi:10.1007/s40436-024-00493-1

Cited by 3 publications

References 115 publications

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Virtual numerical control: an approach towards autonomous manufacturing with a case study in welding

Nasser,

Asy

2024

Int J Adv Manuf Technol

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Today’s customer lifestyles have reshaped their expectations and preferences, driving a growing demand for tailor-made products. While current conventional manufacturing (MNF) systems are robust, they often lack the flexibility needed to accommodate customization. Most MNF systems, despite advances in technology and machinery, still rely on executing predefined instructions, limiting their flexibility. In contrast, human workers excel at handling product variations due to their cognitive abilities, which allow them to perceive, analyze, and make appropriate decisions to adapt to changing conditions. This study introduces virtual numerical control (VNC) as a solution to upgrade MNF systems and overcome these limitations. VNC aims to transform MNF systems into cognitive entities capable of autonomous decision-making, enabling greater flexibility to meet customization demands. To demonstrate the potential of VNC, we implemented it in a welding system as a practical case study. The results showed that VNC enabled the system to operate autonomously. It accurately identified the shape of the objects to be welded, determined the appropriate welding paths, and executed them with high precision, all without human intervention. This highlights the significant potential of VNC technology for broader applications in industrial automation in welding and beyond.

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Virtual numerical control: an approach towards autonomous manufacturing with a case study in welding

Nasser,

Asy

2024

Int J Adv Manuf Technol

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Virtual Numerical Control: An Approach Towards Autonomous Manufacturing with a Case Study in Welding

Asy

2024

Preprint

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Today's customer lifestyles have reshaped their expectations and preferences, driving a growing demand for tailor-made products. While current conventional manufacturing (MNF) systems are robust, they often lack the flexibility needed to accommodate customization. Most MNF systems, despite advances in technology and machinery, still rely on executing predefined instructions, limiting their flexibility. In contrast, human workers excel at handling product variations due to their cognitive abilities, which allow them to perceive, analyze, and make appropriate decisions to adapt to changing conditions. This study introduces Virtual Numerical Control (VNC) as a solution to upgrade MNF systems and overcome these limitations. VNC aims to transform MNF systems into cognitive entities capable of autonomous decision-making, enabling greater flexibility to meet customization demands. To demonstrate the potential of VNC, we implemented it in a welding system as a practical case study. The results showed that VNC enabled the system to operate autonomously. It accurately identified the shape of the objects to be welded, determined the appropriate welding paths, and executed them with high precision, all without human intervention. This highlights the significant potential of VNC technology for broader applications in industrial automation in welding and beyond.

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Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials

Murzin

2024

Metals

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This article explores the integration of artificial intelligence (AI) and advanced digital technologies into laser processing, highlighting their potential to enhance precision, efficiency, and process control. The study examines the application of digital twins and machine learning (ML) for optimizing laser machining, reducing defects, and improving the analysis of laser–material interactions. Emphasis is placed on AI’s role in additive manufacturing and microprocessing, particularly in real-time monitoring, defect prediction, and parameter optimization. Additionally, the article addresses emerging challenges, such as the adaptation of AI models to complex material behaviors and the integration of intelligent systems into existing manufacturing environments. The role of advanced optical technologies, such as free-form optics and diffractive optical elements, is discussed in relation to enhancing laser system adaptability and performance. The article concludes with a discussion on future trends, emphasizing the need for interdisciplinary collaboration to overcome technical and economic complexities while leveraging AI to meet the growing demand for precision and customization in industrial manufacturing.

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Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review

Cited by 3 publications

References 115 publications

Virtual numerical control: an approach towards autonomous manufacturing with a case study in welding

Virtual numerical control: an approach towards autonomous manufacturing with a case study in welding

Virtual Numerical Control: An Approach Towards Autonomous Manufacturing with a Case Study in Welding

Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials

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