Jury :

• M. Alex BALLU, Assistant Professor, HDR, University of Bordeaux
• M. Jean-Yves DANTAN, Professor, Arts et Métiers ParisTech
• M. Giovanni MORONI, Professor, Politecnico di Milano
• Mme Lihong QIAO, Professor, Beihang University
• M. François VILLENEUVE, Professor, University of Grenoble Alpes
• M. Olivier BRUNEAU, Professor, University of Paris-Sud
• M. Luc MATHIEU, Professor, University og Paris-Sud
• M. Nabil ANWER, Professor, University of Paris-Sud

Keywords : Skin Model Shapes, Fabrication additive, Modélisation des écarts géométriques, Analyse Modale Statistique, Transformée en Cosinus Discrète, Analyse Statistique des Formes, Processus Gaussien

Abstract :

Effective modeling of the geometric deviations is critical for Additive Manufacturing (AM). The Skin Model Shapes (SMS) offers a comprehensive framework aiming at addressing the deviation modeling problem at different stages of product lifecycle, and is thus a promising solution for deviation modeling of AM. In this thesis, considering the layer-wise characteristic of AM, a new SMS framework is proposed which characterizes the deviations in AM with in-plane and out-of-plane perspectives. The modeling of in-plane deviation aims at capturing the variability of the 2D shape
of each layer. A shape transformation perspective is proposed which maps the variational effects of deviation sources into affine transformations of the nominalshape. With this assumption, a parametric deviation model is established which manages to capture deviation patterns regardless of the shape complexity. This model is further enhanced with a statistical learning capability to simultaneously learn from deviation data of multiple shapes and improve the modeling accuracy on all shapes. A layer-level investigation of out-of-plane deviation is conducted with a data-driven method. Based on the deviation data collected from a number of Finite Element simulations, two modal analysis methods, Discrete Cosine Transform (DCT) and Statistical Shape Analysis (SSA), are adopted to identify the most significant deviation modes in the layer-wise data. The effect of part and process parameters on the identified modes is further characterized with a Gaussian Process (GP) model. The discussed methods are finally used to obtain high-fidelity SMSs of AM products by deforming the nominal layer contours with predicted deviations and using a graph-based layer connection technique to rebuild the complete non-ideal surface model from the deformed contours.