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48,80 €ISBN 978-3-8440-8904-2Softcover154 pages63 figures240 g21 x 14,8 cmEnglishThesis
January 2023
José Mauricio Urbano Caguasango
Learning-Based Manufacturing Deviation Estimation for Robust Computer-Aided Design
Surface variations are an unavoidable byproduct of any manufacturing process and may lead to deviating part performance and even elevated part rejection rates. Because traditional computer aided-design approaches are aimed towards production of idealized, nominal geometric shapes, the wide geometric and—statistical—variability typical for any manufacturing process remains unrepresented and is frequently ignored during design. Thus, the present work aims at a more realistic design approach and, therefore, develops a collection of computer-aided design strategies for accurate representation, statistical analysis and prospective estimation of surface deviations with validation examples on aeroengine turbine blades.
Keywords: CAD; Machine Learning; Robust Design; Turbine Blade; Manufacturing Deviation
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