# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 1 Introduction
### Literature Review  
Because these contact and non-contact material property measurement methods have individual advantages and disadvantages concerning specimen shape, environment, time, and cost, many researchers have proposed ideas to overcome these drawbacks. [PERSON] et al. [12] proposed a theoretical formula to predict the measurement performance of strain gauge sensors based on specimen deformation through experiments and finite element analysis, showing that measurement values can be predicted by finite element analysis. Some studies have been conducted to improve strain measurement accuracy at cryogenic temperatures [13; 14]. The results demonstrated that contact measurement methods could be used even in extreme environments, such as cryogenic conditions. [PERSON] et al. [15] proposed a method to split the laser beam into two independent sections to improve accuracy and resolution, demonstrating that this method minimizes uncertainties and avoids data loss even in large deformations. [PERSON] et al. [16] proposed a method using harmonic ultrasonic backscatter polar scan (H-UBPS) to measure the cross section of specimens without sample preparation, achieving accuracy similar to commercial methods. [PERSON] et al. [17] proposed chain subset-based DIC technology and a modified second-order shape function (MSSF) to automate tensile tests, increasing measurement accuracy and reducing calculation time. [PERSON] et al. [18] proposed automating strain measurement using a high-resolution camera, confirming that pattern matching and specifying particular ROIs can reduce calculation time.  
[PERSON] et al. [19] proposed using in situ SEM and artificial intelligence algorithms to restore low-resolution images to high-resolution, improving strain measurement accuracy. [PERSON] et al. [20] applied machine learning techniques, including long short-term memory (LSTM) and convolutional neural networks (CNN), to predict the properties of additively manufactured metamaterials. [PERSON] et al. [21] used deep learning to predict the mechanical properties of recycled composites, though the study was limited to recycled CFRTP (Carbon Fiber Reinforced Plastic) materials. [PERSON] et al. [22] proposed an algorithm to identify fractures during tensile testing using a CNN. This method confirmed that using machine learning methods and image subset generation techniques can automatically locate fracture positions. [PERSON] et al. [23] measured strain using mechano-chromic sensors and machine learning, proposing a load prediction algorithm based on color changes due to applied loads. However, they noted limitations in resolution. [PERSON] et al. [24] used deep learning to compensate for lost strain data in DIC measurements but found that the method could not be applied when the specimen's deformation was non-uniform. [PERSON] [25] proposed a method to measure the strain between two marked points on the gauge length using the DIC technique but confirmed that it could not measure local strain.  
As is evident from these previous studies, contact measurement methods have been researched to ensure accuracy in extreme environments but remain challenging to apply to thin sheets [12; 13; 14]. Additionally, non-contact measurement methods have been studied to improve accuracy and reduce calculation times, but they still require preparatory work [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Many researchers have proposed methods to improve the accuracy and efficiency of DIC in strain calculation. Although the DIC method is currently considered the best for material property measurement, the significant time required for spray pattern preparation and the calculation time still need improvement. Moreover, accurately measuring the strain at the fracture site is crucial for predicting fracture behavior in metal materials [26], yet research in this area remains insufficient.