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Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64 Page 9 of 28

Figure 5. The process of automatic programming by a LLM via an AI agent based on ChatGPT-3.5, Copilot, Claude3 and Ernie Bot,
comparing the user interface and model functions of different language models. LLM: Large language model; AI: Artificial intelligence.

higher than that of the non-arc area. Please generate executable Python programs to achieve this, along with
necessary explanations”. The generated programs were seamlessly executed in the Python environment,
producing welding arc contour images without requiring human intervention. This demonstrates the
effectiveness of LLMs in automating complex image-processing tasks. However, the inherent stochasticity
of LLMs can lead to different outputs for the same input, and the generated code might not fit the local
environment. The limitations above can be mitigated by iterative refinement: users can re-input the
generated programs into the LLMs with additional specifications to receive optimized versions. The iterative
process can be repeated as needed until a fully functional program meeting all requirements is obtained.

RESULTS AND DISCUSSION
Weld state classification based on convolution neural network
In pulsed TIG welding, insufficient heating input may result in incomplete fusion of the weld seam, leading
to defects that compromise weld strength and quality. CNNs excel in image-related tasks by directly
processing raw images, thereby obviating the necessity for manually defined features and minimizing
extraction errors. The capability renders the model less sensitive to image clarity, enhancing reliability and
fault tolerance. In this section, CNNs will be applied to establish a classification model for unfused defects in
TIG welding, which can be used for online monitoring of unfused defects during welding, preventing
products with welding defects from flowing out of the production line and posing potential safety hazards
during service.

Data augmentation and training parameters setting
Data augmentation is applied to experimental image data to improve the generalization and robustness of
the CNNs, enabling the model to recognize images with different transformations and distortions . As
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