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.