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Feature Recognition from Topology Optimization for Additive Manufacturing

Encadrant : Nabil Anwer

The research project will investigates Topology Optimization outputs to develop topology reconstruction, partitioning algorithms, feature recognition and surface fitting in the context of Additive Manufacturing.

Contact: Nabil Anwer
E-mail: anwer@lurpa.ens-cachan.fr


Topology Optimization has been used to design and discovery of non-intuitive optimal parts for Additive manufacturing (AM) [1]. The outputs of Topology Optimization tools are a set of thousands of triangular meshes which are not suited for downstream applications such as design modification that requires a feature-based parametric geometry. Moreover, Feature and geometric parameters recognition from Topology Optimization outputs is still a tedious and time consuming process and is highly subjective.
The research project will investigates Topology Optimization outputs to develop topology reconstruction, partitioning algorithms, feature recognition and surface fitting in the context of Additive Manufacturing. A developed approach in the case of high density surface points has been proved to be efficient for segmentation and feature recognition [2]. This approach is based on discrete curvatures estimation and a hybrid partition method (edge detection and region growing). It has been also extended to feature recognition and reverse engineering for virtual machining [3].
The research project will use the previous approach together with Additive Manufacturing process constraints to investigate and define an adapted approach. The closeness of geometric approximation criteria for comparison will also be stated.
[1] Brackett, D., Ashcroft, I., Hague, R. Topology optimization for additive manufacturing. In Proceedings of the Solid Freeform Fabrication Symposium, pp. 348-362, Austin, TX, 2011
[2] H. Zhao, N. Anwer, P. Bourdet, Curvature-based Registration and Segmentation for Multisensor Coordinate Metrology, Procedia CIRP, Volume 10, pp. 112-118, doi:10.1016/j.procir.2013.08.020, 2013
 [3] S. Xu, N. Anwer, C. Mehdi-Souzani, Machining Feature Recognition from In-Process Model of NC Simulation, Computer-Aided Design and Applications, Volume 12, Issue 4, pp. 383-392, 2015