Self-Adaptive Moving Least Squares Measurement Based on Digital Image Correlation

Abstract

Digital image correlation (DIC) is a non-contact measurement technique used to evaluate surface deformation of objects. Typically, pointwise moving least squares (PMLS) fitting is applied to process the noisy data from DIC to obtain an accurate strain field. In this study, a self-adaptive pointwise moving least squares (SPMLS) method was developed to optimize the process of window size selection, thereby attaining superior accuracy in measurements. The premise of this method is that the noise in the displacement field follows white Gaussian noise. Under this assumption, it analyses the random errors and systematic errors of the PMLS method under different calculation window sizes. The optimal size of the calculation window is determined by minimizing the errors. Subsequently, the strain field is computed based on the optimized calculation window. The results were compared with a typical PMLS method. Whether calculating low-gradient strain fields or high-gradient strain fields, the computational accuracy of SPMLS is close to the optimal accuracy of PMLS. This study effectively addresses the inherent challenge of manually selecting window size in the PMLS method.