# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
## 6 Conclusions  
The main idea in this paper is to integrate a specific section view of the grain target with a view of the entire material surface for the tracking of the direction of the metal grain deformation. Several positive results were obtained in this study by successfully preventing performance drops during simultaneous training processes. GD-HBT successfully binarized gray-level metal grain images using a dynamic Gaussian threshold separator. When comparing with those of three existing binarization algorithms, it was shown that GD-HBT outperformed the existing algorithms. This intuitively explains why using the unique properties of each polycrystalline image as the threshold for image binarization reduced the structural degradation of the grain parts. Similarly, the proposed image-stitching algorithm based on BLM successfully reconstructed the view of the entire material surface. The connection of each image at the edge provided an appropriate overlap point as qualitative and quantitative evidence with homoscedasticity was performance on all datasets. The grain-matching algorithm testing using open-source FDs yielded positive results with a relatively low bias and variance. The results are also supported by the SE, \(t\)-statistic, and \(p\)-value values recorded during the testing, which were identical for each FD. Finally, the boundary extraction successfully traced the grain boundary from a binary image. This algorithm effectively distinguished between the grain, grain boundary, and background. Despite the impressive toolset performance, the tracing, tracking, and reconstruction capabilities of the algorithms are not limitless. Ambiguous experimental results were found from the datasets with a significant ratio of the grain targets to the disturbance (the mounting area). Because the algorithm was designed based on binary matching to provide a fast feature detection, the mounting area was extracted without any unique features (blank spots), and thus this area did not provide a sufficient trigger for the algorithm to determine an anchor point. Additionally, considering that binarization is the most important process for achieving a high-precision grain boundary extraction, adjustments should always be made to improve GD-HBT as a binarization tool. The careless implementation of the thresholding values amplifies the possibility of a feature loss during the extraction. Finally, the integrated algorithm was utilized as a tool to quantitatively measure micro-deformation at the metal grain level. This provided an effective method for calculating the direction of the grain deformation by comparing the overlapping area when the material experienced a load. The grain-level assessment using the proposed algorithm found that the microscopic deformation on the grain did not occur in a uniform direction. The are several magnitude deformation variations on the compressive stress area and a local shear stress appears inside the grain on the tensile and combination stress area. It is related to the normal and shear stress on a boundary in a homogeneous body corresponding to the boundary orientation, which is lacking in structural simulation, using FEA to achieve a grain-level assessment. Figure 17: Vector plot of FEA of deflection components in the \(x\) (\(\delta_{x}\)) vs. \(y\) (\(\delta_{y}\)), on structural simulation of 4-point bending test. Ultimately, future research directions include improving image pre-processing to enhance the performance of the integrated toolsets, such as reducing the extraction error and feature loss while avoiding overfitting of the models. In addition, the improvement in matching the shifted quadratic mesh elements before and after the deformation to correlate the corresponding nodes can lead to a sophisticated stress-strain heatmap on the grain level, which is lacking in the literature. This improvement is expected to support further creative applications that require expeditious toolsets to observe fascinating phenomena in material science and engineering.