Outlier removal method for the refinement of optically measured displacement field based on critical factor least squares and subdomain division. (1st May 2022)

• Record Type: Journal Article
• Authors: Wang, Bo; Sun, Chen; Zhang, Keming; Chen, Jubing
• Is Part Of: Measurement science & technology. Volume 33:Number 5(2022)
• Publication Date: 2022-05-01
• Subjects: optical metrology -- full-field displacement -- outlier removal -- critical factor least squares -- abnormal noise; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac476c
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: As a representative type of outlier, abnormal data in displacement measurement has often inevitably occurred in full-field optical metrology and significantly affected further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in an optically measured displacement field. An iterative critical factor least squares (CFLS) algorithm is developed which distinguishes the distance between the data points and the least squares plane to identify the outliers. A successive boundary point algorithm is proposed to divide the measurement domain to improve the applicability and effectiveness of the CFLS algorithm. The feasibility and precision of the proposed method are discussed in detail through simulations and experiments. The results show that the outliers are reliably recognized and the precision of the strain estimation is greatly improved by using these methods.