RSP-Net: A U-Net-Based Hybrid Network for Von Mises Stress Field Prediction of 2D Mechanical Structures

Rapid reconstruction of finite-element stress distributions can support preliminary screening when many related two-dimensional geometries must be examined in a fixed mechanical setting. This study presents a revised RSP-Net for reconstructing normalized RGB renderings of von Mises stress distributions from binary geometry images. The architecture uses U-Net++ as a dense image-to-image backbone and introduces residual Inception refinement only at the input and final full-resolution stages. The training objective combines the mean squared error with a Sobel-based gradient consistency term that emphasizes local variations in the rendered stress pattern and dominates the weighted objective at the selected setting. The evaluation uses a fixed, non-overlapping train/validation/test split, three independent training seeds, closely related U-Net baselines, two trivial baselines, validation-only loss weight selection, and metrics restricted to the structural region. Across three runs, RSP-Net obtains an RGB-RMSE of 0.09727±0.00057, an RGB-MAE of 0.03799±0.00029, and an interior RGB-SSIM of 0.85616±0.00099. Its mean improvements over U-Net++ are modest and are not statistically significant after Holm correction, whereas the gradient consistency term significantly improves RGB-MAE, interior RGB-SSIM, all three high-gradient-region errors, and the Sobel gradient error while increasing the RGB-RMSE by approximately 1.02%. The official color table further yields a normalized scalar RMSE of 0.04568 and a conditional MPa RMSE of 29.83 MPa when the true per-image scale is supplied. Because the targets are independently normalized color renderings, the method reconstructs stress distribution patterns but does not independently predict absolute stresses in MPa.