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In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement's image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multicamera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application. [ABSTRACT FROM AUTHOR]

• Full-field optical measurements are exhibiting random and systematic errors. • Strain errors are evaluated on basis of an archive of full-field DIC measurements. • Two-stage error propagation model predicts non-Gaussian probability distributions. • Predicted parameter uncertainties are larger compared to common methodologies. • The reliability of the inverse identification can be assessed prior to experiments. Digital image correlation (DIC) is a powerful tool for characterising materials and determining material model parameters. To assess the reliability of the full-field measurement-based inverse identification procedures, it is crucial to investigate the impact of the measurement errors on the identified material model parameters. Literature indicates that conventional error propagation models, which rely on Gaussian noise-contaminated data, significantly overestimate the confidence for inversely identified material model parameters, resulting in misleadingly narrow confidence intervals. A more precise assessment of systematic errors originating from the experimental setup leads to an improved prediction of the confidence intervals, but this requires specific information about the DIC equipment, post-processing details, and a skilled experimentalist. In this work, we propose an alternative two-stage error propagation model that yields more realistic confidence interval predictions based solely on a database of past mechanical experiments conducted with the specific stereo DIC system set up in a particular way. We have validated the proposed procedure numerically using the open-hole test and an orthotropic elastic material model. Our predictions reveal a non-Gaussian probability distribution of the inversely identified material model parameters, with confidence intervals considerably wider than those obtained by considering random Gaussian noise. Furthermore, these predictions were experimentally validated in an extensive experimental campaign investigating a pultruded carbon fibre epoxy composite. [Display omitted] [ABSTRACT FROM AUTHOR]