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Title : Bayesian Hierarchical Model for Integrate Multi-resolution Metrology Data
Date : February 4, 2008
Speaker : Haifeng (Heidi) Xia
Affiliation : Department of Industrial and Systems Engineering, Texas A&M University
Abstract
Nowadays, engineers and scientists have multiple sensor technologies available to make almost any measurement. However, different sensors give information at different scales and resolutions. To achieve both efficiency and accuracy, different types of sensors often have to work together for a single measuring task, which results in multi-resolution data.
This talk will present a Bayesian hierarchical model to integrate multi-resolution metrology data for inspecting geometry quality of manufactured parts. The basic idea is to denoise low-resolution data first and then to connect the filtered low-resolution dataset with the high-resolution dataset. This talk will also address the problem of the misalignment between the low- and high-resolution datasets. The solution includes an optimization procedure to roughly align datasets of different resolutions and a neighborhood linkage model to link each high-resolution data point with multiple low-resolution data points. The proposed Bayesian hierarchical model is tested using two-resolution simulated data as well as the measurements of a milled sine-wave part from two coordinate measuring machines of different resolutions. The resulting analysis improves the prediction accuracies by more than 40% over methods that use only single-resolution data.Biosketch
Haifeng (Heidi) is currently a Ph.D. candidate in Industrial and Systems Engineering at Texas A&M University . She received an M.S. degree in 2002 and a B.S. degree in 2000 from Tianjin University , China . Her research focuses on multi-scale/multi-resolution modeling and analysis as well as its applications in dimensional quality control, nanotechnology and remote sensing. She received the 2007 “Quality, Statistics and Reliability” Best Student Paper Award sponsored by INFORMS. Haifeng (Heidi) is a member of INFORMS (the Institute for Operations Research and the Management Sciences), IIE ( Institute of Industrial Engineers ) and ASA(American Statistical Association).
