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Zhang et al. J Mater Inf 2024;4:34 Journal of
DOI: 10.20517/jmi.2024.64
Materials Informatics

Research Article Open Access

Large language models enabled intelligent
microstructure optimization and defects

classification of welded titanium alloys

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Suyang Zhang , William Yi Wang 1,2,* , Xinzhao Wang , Gaonan Li , Yong Ren , Xingyu Gao 3 , Feng
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1
3,*
Sun , Bin Tang , Haifeng Song , Jinshan Li 1,2,*
1
State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China.
2
Innovation Center, NPU Chongqing, Chongqing 401135, China.
3
Institute of Applied Physics and Computational Mathematics, Beijing 100088, China.
* Correspondence to: Prof. William Yi Wang and Prof. Jinshan Li, State Key Laboratory of Solidification Processing, Northwestern
Polytechnical University, No. 127, Youyi West Road, Beilin District, Xi’an 710072, Shaanxi, China. E-mail: wywang@nwpu.edu.cn;
ljsh@nwpu.edu.cn; Prof. Haifeng Song, Institute of Applied Physics and Computational Mathematics, No. 2, Fenghao East Road,
Haidian District, Beijing 100088, China. E-mail: song_haifeng@iapcm.ac.cn
How to cite this article: Zhang S, Wang WY, Wang X, Li G, Ren Y, Gao X, Sun F, Tang B, Song H, Li J. Large language models
enabled intelligent microstructure optimization and defects classification of welded titanium alloys. J Mater Inf 2024;4:34.
https://dx.doi.org/10.20517/jmi.2024.64
Received: 28 Oct 2024 First Decision: 19 Nov 2024 Revised: 9 Dec 2024 Accepted: 16 Dec 2024 Published: 31 Dec 2024

Academic Editor: Chaolin Tan Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
The quick developments of artificial intelligence have brought tremendous attractive opportunities and changes to
smart welding technology. In the present work, a novel model, ConvNeXt, which incorporates the advantages of
convolutional neural networks (CNNs) and vision transformers (ViTs), has been designed to identify welding
defects. The classification accuracy of the pre-trained ConvNeXt based on transfer learning method reaches as
high as 99.52% after 500 iterations of training, while traditional CNNs of MobileNetV2 and ResNet34 achieve
85.94% and 93.41%, respectively. Moreover, the classification performance can be further improved through
dataset optimization based on t-distributed stochastic neighbor embedding (t-SNE). In addition, arc geometrical
features are added as input parameters for building a back propagation neural network to predict the formation of
the weld seam, which has led to a reduction in the maximum prediction error for weld seam thickness from 0.8 to
0.6 mm. Furthermore, out of 28 sets of experimental parameters, only four sets result in errors exceeding 0.2 mm.
It is worth noting that large language models (LLMs) are utilized to facilitate the automated programming for
welding defect recognition, including ChatGPT 3.5, Bing Copilot, Claude3, and ERNIE Bot. LLM-aided automated

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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