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

• URL: http://arxiv.org/abs/2606.26059v1
• Date: Wed, 24 Jun 2026 17:33:41 GMT
• Title: A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
• Abstract summary: SimPhysNet is a novel algorithm that achieves high classification accuracy in laser welding penetration prediction.<n>The core of SimPhysNet is a self-supervised learning paradigm that embeds physical priors into a contrastive learning framework.<n> Experimental results demonstrate that SimPhysNet achieves a classification accuracy of 96.06% using only 200 labelled images.
• Score: 6.163765583080594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 weld quality. To this end, this paper introduces SimPhysNet, a novel algorithm that achieves high classification accuracy in laser welding penetration prediction using only a limited number of labelled images. This approach effectively overcomes the limitations of supervised learning classification algorithms, which are hindered in industrial applications by their dependence on extensive, high-quality labelled data. The core of SimPhysNet is a unique self-supervised learning paradigm that embeds physical priors into a contrastive learning framework. By incorporating a physics-informed neural network (PINN), the model is guided to extract physically meaningful features of the molten pool and keyhole from a large set of unlabelled data, while three image augmentation tasks further enhance its generalization capabilities. Subsequently, a few-shot learning strategy, based on prototypical networks, enables robust classification by constructing class representations from a minimal set of labelled images. Experimental results demonstrate that SimPhysNet achieves a classification accuracy of 96.06% using only 200 labelled images (approximately 5% of the total labelled dataset), which is comparable to the performance of conventional supervised learning algorithms that utilize the entire labelled dataset. This work presents a new, efficient, and highly accurate method, providing the way for the intelligent automation of laser welding.

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