Page 9 - Read Online

Page 2 of 28 Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64

programming technology is applied to develop image stitching programs, achieving unsupervised automatic
stitching of multiple welding tissue images and obtaining clear and wide-field weld ones. These case studies of
deep learning technologies and automated programming based on LLMs set up a solidified building block for smart
welding defect recognition during non-equilibrium solidification.

Keywords: Welding defect recognition, convolutional neural networks, back propagation neural network, large
language models, automated programming

INTRODUCTION
Non-equilibrium solidification, characterized by exceptionally rapid cooling rates that obstruct
[1,2]
thermodynamic equilibrium, assumes a pivotal role in contemporary manufacturing processes .
Depending on the principles of non-equilibrium solidification, welding has become indispensable in
producing materials with specific properties, especially in industries such as shipbuilding, navigation, and
aerospace . However, manual welding is fraught with inefficiencies, high expenditure and inconsistent
[3-5]
performance, which can compromise weld quality . Consequently, the advancement of intelligent welding
[6]
monitoring systems has become imperative for enhancing performance and ensuring reliability in
production . The systems encompass welding defect recognition, welding parameter-geometry
[7-9]
relationship establishment, and automatic programming with the aid of large language models (LLMs) [10,11] .

On the one hand, deep learning algorithms have been considered as one kind of key component of
intelligent welding monitoring systems, which are instrumental in recognizing welding defects [12,13] . Defects
including porosity, cracks, and insufficient fusion can significantly endanger weld quality without timely
identification throughout the welding process [14,15] . Continuous manual oversight of defects in the welding
process is neither practical nor cost-effective . Fortunately, recent advancements in machine vision
[16]
technology have endowed welding robots with the capability to autonomously identify defects [17,18] . At the
nucleus of machine vision resides the deep learning model, with convolutional neural networks (CNNs)
excelling in image processing tasks [19-21] . With multiple layers and deep architecture, CNNs can extract
features from welding images to classify defects [20,22] . Since the vision transformer (ViT) emerged in 2020, the
potential of Transformer-based architectures in computer vision has gained widespread
acknowledgment [23,24] . However, ViT has notable drawbacks compared to CNNs including a large number of
model parameters and high computational demands which pose challenges for achieving lightweight
deployment . Meta AI attributes ViT’s superior performance over CNNs to significant advancements in
[25]
architectural design and optimization techniques, which inspired the creation of ConvNeXt [26,27] . The
innovation strikes a balance between recognition accuracy, storage requirements and computational
efficiency, making ConvNeXt highly suitable for deployment in real-time welding monitoring systems
without compromising production efficiency or imposing significant storage burdens . Meanwhile, the
[28]
ConvNeXt architecture incorporates the exceptional ability of the Transformer framework to capture spatial
and structural relationships in images, enabling it to deliver outstanding performance in welding defect
image recognition tasks.

On the other hand, the intricate and nonlinear interactions among welding heat input, weld joint
microstructures and the subsequent weldment performance present a formidable challenge [29,30] . Precisely
delineating the relationships, achieving accurate predictions of weld geometry and optimizing process
parameters continue to pose substantial hurdles in the field . Despite the extensive accumulation of
[31]
experimental data, the inherent complexity and scale of the information present significant barriers to
uncovering the underlying principles through conventional analysis [32,33] . This is where deep learning
technology provides a transformative solution, leveraging the unparalleled capacity to model nonlinear