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Development of a hybrid method for uncertainty evaluation to provide traceability to dimensional measurements based on X-ray tomography

Grant number: 18/24757-7
Support type:Scholarships abroad - Research
Effective date (Start): December 16, 2019
Effective date (End): August 31, 2020
Field of knowledge:Engineering - Mechanical Engineering - Manufacturing Processes
Principal Investigator:Crhistian Raffaelo Baldo
Grantee:Crhistian Raffaelo Baldo
Host: Wim Dewulf
Home Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
Local de pesquisa : University of Leuven (KU Leuven), Belgium  

Abstract

Industrial Computed Tomography (CT) has been part of dimensional quality control loop for more than a decade. In general, complex-shaped parts with hundreds (or even thousands) of features (hidden features as well) can be holistically checked with great operational advantages over other existing coordinate metrology technologies, such as tactile and/or optical coordinate measuring machines. Nevertheless, due to the complex measurement-error cause system, establishing traceability to the quantity intended to be measured with computed tomography has been a key metrology challenge. This research project proposes developing the foundations for a hybrid approach to assessing the task-specific measurement uncertainty based on CT data, based on computational simulations. The proposed hybrid approach considers blending methods in order to reduce both analytical model complexity and Monte Carlo computational load by exploiting properties of different uncertainty contributors. Modeling of uncertainty propagation will therefore differ as a function of the character of the uncertainty contributors, requiring a deep investigation of the physical factors of the X-rays and the geometrical and physical factors of the CT-based measuring system itself.