製程規劃是機械加工的一項重要工作，目前商用的電腦輔助製造軟體雖然具備完善功能，但需要擁有專業製程知識與加工經驗的人員進行設定，且繁瑣的操作流程使得此類軟體的操作效率低且入門門檻高，因此本論文著眼於自動化製程規劃系統之開發，首先使用深度學習技術自動化由3D幾何模型辨識出五種類別的加工特徵：圓孔特徵、口袋特徵、階梯特徵、盲槽特徵與通槽特徵，接著針對不同的加工特徵選用適當之加工刀具，然後安排合理之加工順序與加工工法，最後自動化產生刀具加工路徑。
本論文除了詳述如何使用經過訓練的深度學習模型進行加工特徵辨識、如何分析加工特徵之幾何資訊進行刀具選用、如何設定加工前處理及如何安排特徵之加工順序與加工工法，也透過Siemens NX的二次開發工具NXOpen建立一套自動化產生加工刀具路徑的電腦系統，並利用兩個3D CAD模型做為實例，驗證所開發系統的實用性。研究結果顯示此系統適用於多種加工特徵，能對特徵產生正確且合理的加工路徑，並大幅節省軟體操作時間。

Process planning plays a crucial role in mechanical machining. While commercial computer-aided manufacturing software offers comprehensive functionalities, it requires individuals with professional process knowledge and machining experience for proper configuration. Additionally, the software's complex operational procedures result in low efficiency and high entry barriers. Therefore, this thesis focuses on the development of an automated process planning system. Initially, deep learning techniques are employed to automatically recognize five types of machining features from 3D geometric models: hole features, pocket features, step features, blind slot features, and through slot features. Subsequently, suitable machining tools are selected for different machining features, followed by organizing appropriate machining sequences and methods. Ultimately, the system automatically generates tool paths for machining.
In addition to proposing the utilization of trained deep learning models for feature recognition, the thesis analyzes geometric information for tool selection, establishes preprocessing procedures, and arranges machining sequences and methods. To accomplish this, the thesis leverages NXOpen, using the application programming interface of Siemens NX to establish an automatic tool path generation system. Two 3D CAD models are utilized as examples to validate the practicality of the developed system. The research demonstrates that the system adapts well to various machining features, is capable of generating accurate and logical machining paths, and significantly reduces software operation time.

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