研究生: |
趙唯志 Wei-Chih Chao |
---|---|
論文名稱: |
以特徵代碼與卷積神經網路進行加工特徵之辨識 Recognizing Machining Features through Feature Codes and Convolutional Neural Networks |
指導教授: |
林清安
Ching-An Lin |
口試委員: |
黃中人
陳羽薰 |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2023 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 162 |
中文關鍵詞: | 3D 模型 、特徵辨識 、深度學習 |
外文關鍵詞: | 3D CAD Model, Feature recognition, Deep learning |
相關次數: | 點閱:75 下載:5 |
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電腦輔助製程規劃是通過自動化規劃零件加工製造的過程,減少手動干預,加快生產計劃的生成速度,即因應零件的改動,快速調整新的製程需求,降低生產成本。電腦輔助製程規劃的其中一個重要部為特徵辨識,其目的為將3D幾何模型的拓樸資訊轉換為加工特徵資訊,減少加工人員的作業時間與精力。雖然目前已有多種特徵辨識方法被提出,但沒有一種是針對具有高度複雜性的單一特徵,這導致在這些方法或神經網路模型難以應用在實際特徵當中,因此本研究提出建立高度複雜性之單一特徵的3D幾何模型的方法,並以圖基法進行特徵提取,將特些特徵提取後的面以特定的編碼規則進行神經網路的訓練,接著使用訓練後的神經網路進行特徵類別的預測。
本文詳述了自動化建立高度複雜性之單一特徵的3D幾何模型的方法、自動化特徵搜尋的方法、特徵編碼之規則及一個有效運用這些特徵代碼的神經網路之架構,並以幾個實例驗證之結果進行探討。
Computer-aided process planning is a process that automates the planning of part manufacturing, reducing manual intervention, accelerating the generation speed of production plans, and swiftly adjusting to new process requirements in response to changes in parts, thereby lowering production costs. One crucial aspect of computer-aided process planning is feature recognition, which aims to convert the topological information of 3D geometric models into machining feature information, thereby reducing the operational time and effort of machining personnel. Although various feature recognition methods have been proposed, none specifically target highly complex individual features. This limitation makes it challenging to apply these methods or neural network models to practical features. Therefore, this research proposes a method for constructing 3D geometric models of highly complex individual features and employs graph-based techniques for feature extraction. The extracted features are then trained using a neural network with specific encoding rules. Subsequently, the trained neural network is used to predict feature categories.
This thesis details the automated method for constructing 3D geometric models of highly complex individual features, an automated feature search method, rules for feature encoding, and an effective neural network architecture that utilizes these feature codes. The results of several case studies are discussed for validation.
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