Automatic hierarchical mid-surface abstraction of thin-walled model based on rib decomposition. (July 2016) - Zhu, Huawei; Shao, Yanli; Liu, Yusheng; Zhao, Jianjun
Highlights: A hierarchical semantic structure is designed for mid-surface model abstraction. An efficient rib recognition method is proposed by using face group pairs. A hybrid method is proposed to handle thin-walled model with complex geometry. An efficient reuse method to reuse the resultant mid-surface model. Abstract: Model simplification is imperative in the process of computer aided design (CAD) and computer aided engineering (CAE) integration. Mid-surface abstraction is the most effective method to simplify the thin-walled models. Many previous research efforts have been focused on the mid-surface abstraction, including the model decomposition based methods, Medial Axis Transform (MAT) based methods and Chordal Axis Transform (CAT) based methods. However, complex thin-walled models cannot be handled well due to the fact that there are some problems including low geometrical precision, poor topological structure, etc., in the above resultant mid-surface models. Especially, these methods are hard to be reused to generate the mid-surface model efficiently. Therefore, a hierarchical semantic mid-surface abstraction method is proposed for the thin-walled model based on rib feature decomposition in this paper. Firstly, a new hierarchical semantic structure is defined and applied on both the thin-walled models and mid-surface models. After that, the model decomposition is conducted based on the identified rib features and the hierarchical semantic information is obtained at the same time. Then, the offset operation and discretization based methods are used to obtain the mid-surface patch for each sub region with different semantic structure respectively. Finally, the mid-surface model with hierarchical semantic information is generated by stitching all discrete patches. Moreover, the above model can be reused to facilitate the rapid mid-surface abstraction of the changed model based on its hierarchical semantic structure. Several examples are given to demonstrate the outperformance of the proposed method.