A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segme...A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.展开更多
A new geometric constraint model is described, which is hierarchical and suitable for parametric feature based modeling. In this model, different levels of geometric information are represented to support various stag...A new geometric constraint model is described, which is hierarchical and suitable for parametric feature based modeling. In this model, different levels of geometric information are represented to support various stages of a design process. An efficient approach to parametric featu-re based modeling is also presented, adopting the high level geometric constraint model. The low level geometric model such as B-reps can be derived automatically from the high level geometric constraint model, enabling designers to perform their task of detailed design.展开更多
基金This project is supported by General Electric Company and National Advanced Technology Project of China(No.863-511-942-018).
文摘A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.
文摘A new geometric constraint model is described, which is hierarchical and suitable for parametric feature based modeling. In this model, different levels of geometric information are represented to support various stages of a design process. An efficient approach to parametric featu-re based modeling is also presented, adopting the high level geometric constraint model. The low level geometric model such as B-reps can be derived automatically from the high level geometric constraint model, enabling designers to perform their task of detailed design.
