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Meg is a member of CAMM. Her work focuses determining the variance and distribution of quantified microstructural features in α+β processed Ti-6Al-4V. Microstructure-property relationships have been developed for Ti-64 via neural network models, leading to predictive property models. The output accuracy of such models is dependent on the variance and distribution of the input data. Estimation of the true variance and distribution can be made given sufficiently large sampling of quantified microstructure present in modeled alloys. The previous generation of manual image processing tools made large dataset processing prohibitive. Using CAMM-developed image processing software, quantitative distributions were measured for several archetypical microstructural features. These distributions have revealed the variation present in a host of Ti-64 microstructures, thus allowing the possibility of estimating the true variability and confidence intervals in the resulting microstructure-property relationships and predictive models.