# Welding Image Data Augmentation Method Based on LRGAN Model
## 5 Conclusions  
This paper proposes a welding image generation method based on the LRGAN model. This method utilizes an adversarial learning mechanism to deeply mine the features from a small number of real samples, and then generates similar welding data, effectively addressing the problem of insufficient samples in the welding industry. The results of various comparative experiments show the following:  
1. The least squares loss function is used to replace the original cross-entropy loss function, leading to a more stable training process. The discriminator becomes stricter and more precise in classification, making the generated images from the generator more closely resemble the original images in terms of structure and details. The least squares loss function contributes the most to controlling the SSIM and bringing its value closer to 1.
2. Spectral normalization has a more significant effect on improving PSNR. By constraining the discriminator's Lipschitz continuity, it increases the PSNR value and enhances the model's ability to capture the overall clarity of the image.
3. The residual structure is more helpful in reducing the FID value. It helps retain the complex texture structures of the image and reduces mode collapse.
4. The number of real data samples directly impacts the quality of the data generated by the LRGAN model. Comparative experiments with different quantities of real samples show that when the number of real data is 300, the LRGAN model achieves optimal overall performance, and the SSIM value is closer to 1. This indicates that, under this data quantity, the LRGAN model performs optimally and demonstrates good adaptability and generalization ability for small sample sizes.  
However, this study still has certain limitations. Firstly, the current model is primarily validated in the specific industrial scenario of welding images, and its adaptability in other types of industrial images or general image generation tasks still requires further exploration. Secondly, while spectral normalization and residual structures improve model performance, they also increase training complexity and computational cost, which may pose challenges for deployment on resource-constrained edge devices.  
Future research can be pursued in the following directions:  
1. Explore more lightweight network architectures to balance performance and deployment efficiency.
2. Introduce self-supervised learning or contrastive learning mechanisms to further enhance the model's learning ability under ultra-small sample conditions.
3. Extend this method to the generation of multi-modal or 3D welding data to meet a broader range of industrial needs.  
Conceptualization, Y.W. and Z.D.; methodology, Z.D.; software, Z.D.; validation, Z.D.; investigation, Z.D., Q.Z. and Z.H.; writing--review and editing, Y.W. and Z.D.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.