Abstract

Digital Image Correlation (DIC) is a powerful non-contact strain measurement tool for large strain, full-field strain measurement that is easily coupled with material testing. Full-field strain measurements give simulation engineers a plethora of data that can be accurately used to characterize a material. This wealth of data can be used to select and calibrate an advanced material model, particularly for materials that exhibit strain localization during uniaxial testing. Effectively using the data can enable an engineer to calibrate a more accurate material model. In this work we perform a digital image correlation and material modeling study on an impact-modified Polycarbonate (PC) material to investigate how to best use full field strain data. The PC material exhibits significant post-yield strain localization (necking) during tensile testing, making the DIC method extremely useful in characterizing mechanical behavior post necking. Additionally, the PC material has significant softening after yield, making accurate material models more difficult to calibrate. DIC is most useful when the engineer can extract all necessary information for an advanced material model. This paper aims to demonstrate which information can be easily used and how to best use it. We analyze the DIC data to extract strain data from different regions of tensile samples, including a virtual extensometer, average strain in the necked region, and average strain in the un-necked region of the samples. We use the strain data to select and calibrate a material model with a single element method, exploring how to use the data from different sample regions to calibrate a more accurate model. Different strain measurements from the same test are utilized to improve the material model. We also use an inverse calibration method to capture the strain and ensure that the material model is as accurate as possible for the simulation, and then simulate an inhomogeneous test specimen and loading to validate the experiment.