New approach to evaluate speckle pattern quality in digital image correlation for estimation of unknown parameters

In this paper, a new criterion for evaluating speckle pattern in DIC method is proposed. In our study, the authors assumed that the identification of unknown parameters by inverse analysis using strain field measurement based on digital image correlation (DIC) is promising because there are many possible applications: in-process monitoring of metal working, extraction of implicit know-how of manufacturing process, and identification of mechanical parameters not only in industrial materials but also in inhomogeneous biological materials. To establish this method, it is difficult to construct a useful physical model, an efficient reverse analysis process, and the measurement quality evaluation criteria. As a first step, this paper focuses on evaluating the quality of transitional speckle patterns on the surface of objects during large deformation. Although the quality of the speckle pattern greatly affects the number of search errors, it is difficult to evaluate the quality reliably by a single criterion such as the mean subset fluctuation or Shannon entropy proposed as the quality evaluation criterion. Therefore, a new criterion FE is proposed and its characteristic is investigated through experiments.