Image processing algorithm for mechanical properties testing of high-temperature materials based on time-frequency analysis

Image processing algorithms based on time-frequency analysis are frequently used in testing and researching the mechanical properties of high-temperature materials, especially in aerospace, engines, and ships. However, due to the complexity of the high temperature environment itself, there are many factors that affect the measurement accuracy, among which the existence of thermal airflow disturbance has a particularly significant impact on the digital image correlation method. This paper mainly focuses on two aspects of high temperature edge detection and digital image correlation. On the one hand, the existing algorithm is improved to make it suitable for material property detection in high temperature environment. And relevant experiments are carried out to verify the feasibility and accuracy of the algorithm. The full-field coefficient caching method for fitting initial values proposed in this paper has the premise that the accuracy is comparable to that of the reverse combined Gauss-Newton matching algorithm, but the running speed is improved by about 10%, and the matching accuracy of both is obviously higher than that of the surface fitting algorithm.