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Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64 Page 3 of 28

[34]
relationships . Integrating simulation techniques with deep learning models facilitates the automatic
identification of correlations between welding parameters and weld seam microstructures, enabling accurate
[35]
predictions of weldment performance . The approach not only optimizes process parameters and weld
quality but also significantly reduces the reliance on costly and time-intensive experimental trials, pushing
the boundaries of welding technology .
[36]
Moreover, the development of the monitoring systems involves sophisticated image processing and neural
network models, demanding considerable human effort and advanced programming expertise [37-40] . In order
to tackle the complexity of programming, the LLMs have emerged as powerful tools for automating
programming tasks [40-43] . Based on transformer architecture, LLMs utilize self-attention mechanisms to
capture contextual dependencies in programming, thereby enabling the generation of coherent and efficient
outputs [44-46] . The models including GPT-3.5, BERT, and Copilot have demonstrated remarkable potential in
automating code generation and optimization tasks [47,48] . The capacity of programming explanation and
generation renders the LLMs invaluable assets for programming automatic welding systems [49,50] .

To address the aforementioned challenges, a progressive monitoring system for the intelligent welding
industry is presented in the research, harnessing wide-ranging image data amassed from tungsten inert gas
(TIG) experiments. In Section “Weld state classification based on convolution neural network”, CNNs are
employed for image data classification, followed by the evaluation of classification metrics and feature
visualization to enhance the classifiers. In Section “Weld seam forming prediction on back propagation
neural network”, backpropagation neural networks (BPNNs) are applied to establish a mapping between
welding process parameters and welding formation geometry with model performance further ungraded by
incorporating arc geometric features. In Section “Automatically programming based on LLMs”, LLMs are
introduced to support programming tasks in image processing, with the code generation capabilities
evaluated for arc contours extracting and welding image stitching.

MATERIALS AND METHODS
Artificial intelligence agents have been effectively integrated into the workflow of smart welding as assistants
in the field of intelligent manufacturing . As illustrated in Figure 1, LLMs are utilized to automate
[51]
programming based on welding image data gathered from high-throughput experiments, with the objective
[52]
of optimizing the defect detection process during welding of titanium alloys . The methodologies
encompass titanium alloy tube-to-plate welding experiments, deep learning techniques and automatic
programming of intelligent welding based on LLMs.

Tube-to-tube-sheet welding experiments for titanium alloy
Owing to the high chemical reactivity of titanium and the propensity to adsorb hydrogen, oxygen and
nitrogen at elevated temperatures, conventional welding techniques such as manual metal arc welding, gas
[53]
welding and CO gas-shielded welding are deemed unsuitable . TIG welding is employed in the research,
2
with the arc generated between the tungsten electrode and the workpiece to melt the metal, while the inert
gas is introduced around the electrode to safeguard the metal and maintain arc stability . The schematic
[54]
diagram of a TIG tube-to-tube-sheet welding robot system is shown in Figure 2. The heat exchanger tubes,
tube sheet and welding wire are all composed of TA2, which is an α-phase titanium alloy with excellent
[55]
corrosion resistance and cold workability . The chemical composition of TA2 is presented in Table 1. The
heat exchanger tubes feature an outer diameter of 10 mm and a wall thickness of 1.5 mm. The tube sheet,
measuring 100 mm in thickness, includes a 1 mm × 1 mm chamfer at a 45° angle. The diameter of the
welding wire is 0.8 mm.