A practical and effective regularized polynomial smoothing (RPS) method for high-gradient strain field measurement in digital image correlation. (October 2019)

• Record Type: Journal Article
• Authors: Li, Xin; Fang, Gang; Zhao, Jiaqing; Zhang, Zhengming; Sun, Libin; Wang, Haitao; Wu, Xinxin
• Is Part Of: Optics and lasers in engineering. Volume 121(2019)
• Page Start: 215
• Page End: 226
• Publication Date: 2019-10
• Subjects: Digital image correlation -- High-gradient strain -- Displacement smoothing; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2019.04.017
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A regularized polynomial smoothing (RPS) method based on Tikhonov regularization is proposed to improve previous local Hermite (LH) method and measure noisy high-gradient strain field (HSF). RPS is nearly insensitive to window size and produces lower error than point-wise least-squares (PLS) method in resolving local HSF. In match/overmatch case, Tikhonov regularization in RPS greatly decreases the degree of the morbidity caused by ill-posedness in PLS and ensure the smoothness and stability of solution. Abstract: Digital image correlation (DIC) is an effective tool for studying the deformation of tested materials. However, high-gradient strain field (HSF) measurement remains a challenging issue in DIC, because multifold factors have a significant effect on the first correlation calculation step of the original displacement and the exact strain reconstruction in the second post-processing step. In this study, we focus on the second step. The formerly proposed local Hermite has been proven as an accurate and robust method, yet substantial programming work is involved in constructing a Hermite element. To solve this issue, a regularized polynomial smoothing (RPS) method is proposed, which replaces the Hermite function of the polynomial. The RPS applies the same kernel function as the point-wise least-squares (PLS) method for easy implementation. Tikhonov regularization and the generalized cross-validation ( GCV ) function are also used to decrease unavoidable noise and weaken the dependence on window size. The experiments demonstrate that, in the matched/overmatched case, Tikhonov regularization in RPS can greatly decrease the degree of morbidity caused by ill-posed problems in the PLS method, thereby ensuring smoothness and stability of the solution. Moreover, the influences of window size and polynomial order of both the RPS and PLS are also closely studied. In conclusion, the proposed RPS provides a practical, robust and accurate method for HSF measurement.