在一般薄殼元件上，凸起物是常見的特徵，無論是功能性物件或者是造型物件皆是在一模型上非常重要的特徵物件，而凸起物又可再細分成肋、管、柱，以及其它較複雜形狀如兩塑膠殼件接合處的卡榫亦或是其它造型結構，以上特徵易造成品質不良的網格建構，進而影響CAE分析之準確度，若是能在事先將殼件基底外之凸起物作辨識前置處理，方能針對各種不同形狀之特徵，個別進行網格化，以達成改善網格品質。而本研究基於B-rep資料結構，發展出一特徵辨識技術，針對一般塑膠薄殼元件，可辨識殼件基底上之凸起面，並將相鄰凸起面群組化，同時記錄相關資訊與分類。此一系列的過程有別於市售軟體需以近手動方式進行辨識，而是以自動化方式將殼件基底與凸起物資料辨識分離。而本研究將以22個實際案例來驗證此辨識演算法之準確率及可靠性。;In general thin shell models, extrusion is a very common feature, and is an important structure on a model for both functional and modeling purposes. Extrusion can be roughly classified into two types. One is easy structure like rib, tube, and column, and the other is more complicated like the hold-open device, which is between two plastic parts. Such features sometimes tend to generate poor-quality meshes and lead to the deterioration of CAE analysis. If a feature recognition pre-process for extrusion structure is available, then the mesh quality can be improved by applying better types of meshes for such a structure. This research presents an approach based on the B-rep data structure for an extrusion recognition of plastic thin shell parts. It can recognize and record extrusion surface independently from thin shell parts. For each grouping data, we analyze and sort the neighboring faces which represent a group of extrusion. The proposed algorithm is different from commercial software which is primarily manual-operated. Our study can recognize and separate the extrusion feature automatically. Twenty-two models are applied to test the accuracy and reliability of the proposed extrusion recognition algorithm.
