Study on experimental characterization of carbon fiber reinforced polymer panel using digital image correlation: A sensitivity analysis

Abstract

In this work, the experimental characterization of polymer–matrix and polymer based carbon fiber reinforced composite laminate by employing a whole field non-contact digital image correlation (DIC) technique is presented. The properties are evaluated based on full field data obtained from DIC measurements by performing a series of tests as per ASTM standards. The evaluated properties are compared with the results obtained from conventional testing and analytical models and they are found to closely match. Further, sensitivity of DIC parameters on material properties is investigated and their optimum value is identified. It is found that the subset size has more influence on material properties as compared to step size and their predicted optimum value for the case of both matrix and composite material is found consistent with each other. The aspect ratio of region of interest (ROI) chosen for correlation should be the same as that of camera resolution aspect ratio for better correlation. Also, an open cutout panel made of the same composite laminate is taken into consideration to demonstrate the sensitivity of DIC parameters on predicting complex strain field surrounding the hole. It is observed that the strain field surrounding the hole is much more sensitive to step size rather than subset size. Lower step size produced highly pixilated strain field, showing sensitivity of local strain at the expense of computational time in addition with random scattered noisy pattern whereas higher step size mitigates the noisy pattern at the expense of losing the details present in data and even alters the natural trend of strain field leading to erroneous maximum strain locations. The subset size variation mainly presents a smoothing effect, eliminating noise from strain field while maintaining the details in the data without altering their natural trend. However, the increase in subset size significantly reduces the strain data at hole edge due to discontinuity in correlation. Also, the DIC results are compared with FEA prediction to ascertain the suitable value of DIC parameters towards better accuracy.