基于物理信息神经网络的双速双分辨数字图像相关方法

Dual-Speed, Dual-Resolution Digital Image Correlation Method Based on Physics-Informed Neural Networks

  • 摘要: 由于内存、带宽限制,常规高速相机难以同时满足高帧率和高分辨的测试需求. 在需要高速图像采集的动态加载测试中(例如爆炸、冲击试验),限制图像分辨率会导致DIC算法的位移场解析精度不足. 针对以上问题,提出了一种基于物理信息神经网络的双速双分辨数字图像相关方法(PINN-DSDR-DIC). 该方法利用分光光路实现双速双分辨稀疏图像采集,并利用算法实现高速高分辨位移场的高精度解析. 算法将位移的时空间连续性以及灰度一致性融入PINN中,通过网络参数优化求解高速高分辨高精度的位移场. 通过数值实验证明了PINN-DSDR-DIC算法的可行性,并分析了影响算法精度的因素. 最后,搭建了分光光路系统,并通过带凹槽核石墨的四点弯实验对PINN-DSDR-DIC方法进行应用验证.

     

    Abstract: Due to memory and bandwidth limitations, it is difficult for conventional high-speed cameras to meet simultaneously the demands of high frame rate and high resolution testing. In dynamic loading tests that require high-speed image acquisition (e.g., explosion and impact tests), limiting the image resolution will lead to insufficient displacement field resolution accuracy of the DIC algorithm. To address the above problems, a dual-speed, dual-resolved DIC method was proposed based on PINN (PINN-DSDR-DIC). The method was arranged to realize dual-speed dual-resolution sparse image acquisition with a spectral optical path, and to realize high-precision resolution with an algorithm for high-speed high-resolution displacement field. Firstly, incorporating the temporal and spatial continuity of displacement as well as the gray scale consistency into PINN, the algorithm was designed to solve the high-speed, high-resolution and high-precision displacement field by optimizing the network parameters. And then, some numerical experiments were carried out to prove the feasibility of the PINN-DSDR-DIC algorithm and to analyze the affecting factors on the accuracy of the algorithm. Finally, a spectral path system was built and the PINN-DSDR-DIC method was applied and verified based on four-point bending experiments of graphite with grooved nuclei.

     

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