Showing papers by "Takeo Kanade published in 2022"
TL;DR: In this article , the authors proposed a method that does not require a reference signal and frequency analysis to obtain the stress amplitude distribution with comparable or higher accuracy than that obtained using self-correlation lock-in thermography.
Abstract: Abstract Background In self-correlation lock-in thermography for thermoelastic stress analysis (TSA), the acquisition position of the reference signal affects the accuracy of the obtained stress amplitude distribution. When the reference signal is not large enough compared to the noise, the stress amplitude distribution may be incorrect. Objective This study proposes a method that does not require a reference signal and frequency analysis to obtain the stress amplitude distribution with comparable or higher accuracy than that obtained using self-correlation lock-in thermography. Methods An observation matrix is generated from the temporal variation across all thermographic pixels to describe the thermal fluctuations due to stress. Thereafter, stress amplitude distribution and the original load signal are extracted from the observation matrix using singular value decomposition (SVD). The proposed method is called SVD thermo-component analysis. To investigate the effectiveness of the proposed method, the reconstructed load signal and stress distribution are obtained from the captured thermal images for the specimen under a sinusoidal load. Results The stress amplitude distribution obtained using the proposed method is equivalent to that obtained using conventional lock-in thermography with the original load signal as the reference signal. In addition, the reconstructed load signal obtained using the proposed method successfully represents the original load signal. Conclusions SVD thermo-component analysis does not require prior knowlege of the evaluated mechanical structure to select a suitable reference-signal acquisition position as in self-correlation lock-in thermography. Therefore, the proposed TSA method reduce analysis failures compared to the conventional method.