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AI models predict welding quality across laser and TIG processes · 5 sources tracked

By PulseAugur Editorial · [5 sources] ·

Researchers have developed advanced deep learning models for predicting weld quality in laser and TIG welding processes. One model utilizes a multi-task spatiotemporal deep neural network to predict penetration depth and morphology from weld pool images, achieving high accuracy. Another approach focuses on unsupervised domain adaptation to enable models trained on one welding process to perform well on another, significantly reducing the need for extensive re-labeling. A third method employs self-supervised learning with physics-informed neural networks to predict laser welding penetration using minimal labeled data, demonstrating comparable performance to fully supervised methods. AI

IMPACT These advancements could lead to more automated and precise welding processes, reducing defects and material waste in industrial manufacturing.

RANK_REASON Multiple research papers published on arXiv detailing new AI models for welding quality prediction.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

AI models predict welding quality across laser and TIG processes · 5 sources tracked

COVERAGE [5]

  1. arXiv cs.AI TIER_1 English(EN) · Sen Li, Haichao Cui, Chendong Shao, Yaqi Wang, Xinhua Tang ·

    A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding

    arXiv:2606.26260v1 Announce Type: cross Abstract: In laser penetration welding, the assessment of penetration state and weld seam morphology plays a crucial role in determining the weld quality. This paper presents a comprehensive introduction of the innovative muti-task deep lea…

  2. arXiv cs.AI TIER_1 English(EN) · Xinhua Tang ·

    A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

    Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under domain shift, such as when transferring models between welding processes with distinct physical mechanisms:for instance, from arc-d…

  3. arXiv cs.AI TIER_1 English(EN) · Haichao Cui ·

    A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

    The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring weld quality. To this end, this paper introduces…

  4. arXiv cs.CV TIER_1 English(EN) · Sen Li, Xiaoying Liu, Xiaojian Xu, Chendong Shao, Yaqi Wang, Ling Lan, Xinhua Tang, Haichao Cui ·

    A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

    arXiv:2606.26059v1 Announce Type: new Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring …

  5. arXiv cs.CV TIER_1 English(EN) · Sen Li, Haichao Cui, Chendong Shao, Yaqi Wang, Xinhua Tang ·

    A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

    arXiv:2606.26078v1 Announce Type: new Abstract: Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under domain shift, such as when transferring models between welding processes with disti…

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