{"54974":{"#nid":"54974","#data":{"type":"event","title":"Statistical Shape Analysis of Manufacturing Data","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E Statistical Shape Analysis of Manufacturing Data\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E Professor Enrique del Castillo\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWe show how Statistical Shape\nAnalysis, a set of techniques used to model the shapes of biological and other\nkind of objects in the natural sciences, can be used also to model the\ngeometric shape of a manufactured part. We first review Procrustes-based\nmethods, and emphasize possible solutions to the basic problem of having\ncorresponding, or matching, labels in the measured ``landmarks\u0022, the\nlocations of the measured points on each part acquired with a CMM or similar\ninstrument. The analysis of experiments with shape responses is discussed\nnext.\u0026nbsp; The usual approach in practice is\nto estimate the form error of the part and conduct an ANOVA on the form errors.\nInstead, an F ANOVA test due to Goodall and a new permutation ANOVA test for\nshapes are presented. Real data sets as well as simulated shape data of\ninterest in manufacturing were used to perform power comparisons for 2 and 3\ndimensional shapes. The ANOVA on the form errors was found to have poor\nperformance in detecting mean shape differences in circular and cylindrical\nparts. The ANOVA F test and the Permutation ANOVA\u0026nbsp; test provide highest power to detect\ndifferences in the mean shape. It is shown how these tests can also be applied\nto general \u0022free form\u0022 shapes of parts where no standard definition\nof form error exists in manufacturing practice. New visualization tools,\nincluding main effect and interaction plots for shapes and deviation from\nnominal plots are presented to help interpreting the results of experiments\nwhere the response is a shape. \u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp; \u003C\/p\u003E\u003Cp\u003EDr. Castillo is\na Distinguished Professor of Industrial Engineering. \u0026nbsp;Dr. Castillo also holds a joint appointment\nwith the Department of Statistics. \u0026nbsp;He is\nan author of over 85 journal papers and 2 textbooks and a former NSF CAREER awardee,\nFulbright Scholar, and the Editor in Chief of the Journal of Quality Technology\n(2006-2009).\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EStatistical Shape Analysis of Manufacturing Data\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Statistical Shape Analysis of Manufacturing Data"}],"uid":"27187","created_gmt":"2010-03-17 06:25:46","changed_gmt":"2016-10-08 01:51:05","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-03-19T13:00:00-04:00","event_time_end":"2010-03-19T14:00:00-04:00","event_time_end_last":"2010-03-19T14:00:00-04:00","gmt_time_start":"2010-03-19 17:00:00","gmt_time_end":"2010-03-19 18:00:00","gmt_time_end_last":"2010-03-19 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}