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When examining the literature on the adhesion strength of impact ice, there have been a wide range of methodologies tried to measure the required stresses to induce interfacial delamination. Utilizing the Icing Research Tunnel at the NASA Glenn Research Center to generate the impact ice required for this work, several different mechanical tests have been and are being developed to determine the stresses along the interface between ice and coupon. This set of tests includes the technical mature modified lap joint test which has been used to conduct ice adhesion studies through a wide sweep of icing conditions. To conduct in situ ice adhesion measurements inside of the Icing Research Tunnel, several new experiments are currently being developed to make ice adhesion measurements during and immediately after ice accretion.

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The microstructure of impact ice has been shown to correlate with the adhesion strength of ice. Machine vision techniques are explored as a method of decreasing analysis time. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods.