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TITLE: Statistical Shape Analysis of Manufacturing Data
SPEAKER: Professor Enrique del Castillo
ABSTRACT:
We show how Statistical Shape Analysis, a set of techniques used to model the shapes of biological and other kind of objects in the natural sciences, can be used also to model the geometric shape of a manufactured part. We first review Procrustes-based methods, and emphasize possible solutions to the basic problem of having corresponding, or matching, labels in the measured ``landmarks", the locations of the measured points on each part acquired with a CMM or similar instrument. The analysis of experiments with shape responses is discussed next. The usual approach in practice is to estimate the form error of the part and conduct an ANOVA on the form errors. Instead, an F ANOVA test due to Goodall and a new permutation ANOVA test for shapes are presented. Real data sets as well as simulated shape data of interest in manufacturing were used to perform power comparisons for 2 and 3 dimensional shapes. The ANOVA on the form errors was found to have poor performance in detecting mean shape differences in circular and cylindrical parts. The ANOVA F test and the Permutation ANOVA test provide highest power to detect differences in the mean shape. It is shown how these tests can also be applied to general "free form" shapes of parts where no standard definition of form error exists in manufacturing practice. New visualization tools, including main effect and interaction plots for shapes and deviation from nominal plots are presented to help interpreting the results of experiments where the response is a shape.
Bio:
Dr. Castillo is a Distinguished Professor of Industrial Engineering. Dr. Castillo also holds a joint appointment with the Department of Statistics. He is an author of over 85 journal papers and 2 textbooks and a former NSF CAREER awardee, Fulbright Scholar, and the Editor in Chief of the Journal of Quality Technology (2006-2009).
