Template-based reverse engineering of parametric CAD models from point clouds
Template-based reverse engineering of parametric CAD models from point clouds
Résumé
Even if many Reverse Engineering techniques exist to reconstruct real objects in 3D, very few are able to deal directly and efficiently with the reconstruction of the editable CAD models of assemblies of mechanical parts that can be used in the stages of Product Development Processes (PDP). In the absence of suitable segmentation tools, these approaches struggle to identify in the reconstructed model the different parts that make up the assembly. The thesis aims to develop a new Reverse Engineering technique for the reconstruction of editable CAD models of mechanical parts’ assemblies. The proposed method uses Simulated Annealing-based fitting and the optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interface detection. The originality lies in the exploitation of multimodal data (e.g. clouds of points, a database of CAD models, and store best configurations for the fitting process). The thesis presents a two-stage modular approach. The first step is to collect within the multimodal data a set of characteristics that contribute to the Simulated Annealing-based new Reverse Engineering technique and for the identification of interfaces and parts. In a second step, it is necessary to merge this information with the help of transformation operators working in a common space. The method integrates sensitivity analysis to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be fitted. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach demonstrates good capacities to help to maintain the coherence between a product/system and its digital twin.
Even if many Reverse Engineering techniques exist to reconstruct real objects in 3D, very few are able to deal directly and efficiently with the reconstruction of the editable CAD models of assemblies of mechanical parts that can be used in the stages of Product Development Processes (PDP). In the absence of suitable segmentation tools, these approaches struggle to identify in the reconstructed model the different parts that make up the assembly. The thesis aims to develop a new Reverse Engineering technique for the reconstruction of editable CAD models of mechanical parts’ assemblies. The proposed method uses Simulated Annealing-based fitting and the optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interface detection. The originality lies in the exploitation of multimodal data (e.g. clouds of points, a database of CAD models, and store best configurations for the fitting process). The thesis presents a two-stage modular approach. The first step is to collect within the multimodal data a set of characteristics that contribute to the Simulated Annealing-based new Reverse Engineering technique and for the identification of interfaces and parts. In a second step, it is necessary to merge this information with the help of transformation operators working in a common space. The method integrates sensitivity analysis to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be fitted. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach demonstrates good capacities to help to maintain the coherence between a product/system and its digital twin.
Origine : Version validée par le jury (STAR)