基于SANet的数字光弹性应力场分析方法

Digital Photoelastic Stress Field Analysis Method Based on SANet

数字光弹性应力分析通常先采用相移法计算等差线和等倾线,再通过应力分离算法计算出各应力分量。针对传统分阶段计算方式中存在的相位展开困难和鲁棒性差等问题,提出一种基于SANet的应力分析深度学习模型,该模型无需相位展开和应力分离操作即可在第一主应力方位角固定的场景下从单幅等差线包裹图直接解算出3个应力分量。在圆盘仿真与真实测试数据上进行验证,结果显示,所提模型解算的3个应力分量均方误差均值为0.027,且抗噪能力显著优于传统分阶段处理方法。所提出的应力分析模型在保证精度的同时,提升了应力分量计算的效率和鲁棒性。

Objective The digital photoelasticity method combines optics and digital image processing technology. Digital image processing and numerical calculation can help achieve accurate analysis of optical interference patterns, thereby obtaining accurate stress distribution information. It is of significance for stress analysis problems in scientific research, engineering design, and material testing fields. However, the current digital photoelasticity method adopts a divide and conquer approach, dividing the entire stage into several substeps such as phase shifting, phase unwrapping, and stress separation.Each substep requires high experimental environments such as noise, and the calculation accuracy of each stage is limited by the calculation results of the previous stage. Thus, immediate errors generated in each stage will be introduced into the final stress component. With the development of artificial intelligence, deep learning has gradually been applied to digital photoelasticity methods. However, current deep learning models only involve some research on calculating stress differences, and traditional stress separation methods are still needed to calculate normal stress and shear stress.Therefore, we propose a multi-branch deep learning model based on an encoder-decoder and a simulation dataset construction method for stress analysis tasks. This model improves the efficiency and robustness of stress component calculation while ensuring accuracy.Methods The proposed method mainly utilizes the feature extraction ability of convolutional neural networks. Based on the improvement of UNet, residual blocks are employed to replace the convolutional modules of the encoder and decoder,accelerating the convergence speed and improving the feature expression ability of the model. Multiple output layers are added in the output part to adapt to the stress component calculation task. Meanwhile, a simulation dataset is generated using the theory formula of radial compression discs, and the dataset is expanded by operations such as rotation,translation, and cropping to provide data-driven support for SANet. Finally, L2 loss is adopted as the loss function for each branch of the neural network, and the weighted sum of the loss functions for the three branches is leveraged to calculate the total loss.Results and Discussions The experimental results on the simulation test set indicate that SANet can calculate the normal stress and shear stress(Fig. 5). In the noise experiment, our model achieves the highest MSE, PSNR, and SSIM(Table 3). The model is tested using a noise test set with a mean of 0 and an increasing standard deviation(Fig. 7), which indicates that our model has strong noise tolerance. Finally, tests are conducted on real data(Fig. 8). Compared with traditional phased processing methods, this method can avoid the phase unwrapping and stress separation stages that are prone to errors, and achieve stress component calculation in one step.Conclusions We propose a deep learning method for calculating stress components. This method introduces residual connections based on UNet and changes the output part to a multi-branch output structure to adapt to the stress component calculation task. To train the model, we construct a simulation training set using the radial compression disc formula and data augmentation methods. Additionally, the comparison is conducted between the two phased methods and the proposed method on simulation test sets, noise test sets, and real test data. The results show that compared to traditional phased processing methods, SANet has the highest accuracy and better robustness in calculating stress components.