隨著科技的不斷進步，金屬加工已經邁向自動化、數位化和智慧化，使金屬工業蓬勃發展並推向全新領域，然而，儘管自動化技術不斷提升，但從事金屬加工行業人員逐年下降，導致金屬加工人才越來越稀缺。在製造領域中，雖然電腦輔助製造軟體功能越來越多且能預先設定參數，但繁瑣的操作過程使人員規劃時間增加且入門門檻亦較高。為了改善此問題，本論文著眼於金屬加工製程規劃之科學化分析與標準流程制訂，首先使用預設的刀具參數進行粗加工體積切削，將零件的大部分胚料移除，接著以切削概念將零件劃分成數個區域進行精加工，然後安排切削工法及切削順序，並產生加工路徑。
本論文除了詳述如何設定加工前處理、如何安加工順序與加工工法、如何分析切削後之參數進行選用刀具、如何使用殘料進行二次選刀及如何將零件分區域加工，並利用兩個3D CAD零件做為實例，驗證所規劃之標準流程的可行性。研究結果顯示此流程適用於複雜零件，並能簡化加工製程規劃時間，此外，亦能找出最佳切削刀具組合，進而有效縮短切削時間。

With the continuous advancement of technology, metal processing has moved towards automation, digitization, and intelligence, propelling the metal industry into new frontiers. However, despite the continuous improvement in automation technology, the number of personnel engaged in the metal processing industry has been decreasing year by year, leading to an increasing scarcity of metal processing talent. In the manufacturing field, although computer-aided manufacturing software has increasingly sophisticated features and can preset parameters, the tedious operation process increases the planning time for personnel, and the entry threshold is also higher. To address this issue, this thesis focuses on the scientific analysis and standard process formulation of process planning for metal processing. It begins by using default tool parameters for rough machining volumetric cutting, removing most of the workpiece's material. The workpiece is then subdivided into several regions for precision machining using basic metal cutting concepts. Subsequently, cutting methods and cutting sequences are arranged, and machining paths are generated.
In addition to detailing how to set up preprocessing, arrange machining sequences and methods, analyze post-cutting parameters for tool selection, use residues for secondary tool selection, and how to process the workpiece by subdividing it into regions, this thesis utilizes two 3D CAD parts as examples to validate the feasibility of the planned standard process. The research results show that this process is suitable for complex parts, can simplify the machining process planning time, and can identify the optimal cutting tool combination, thereby effectively reducing cutting time.

[1] Egon Ostrosi, Jean-Bernard Bluntzer, Zaifang Zhang and Josip Stjepandić (2019), “Car style-holon recognition in computer-aided design,” Journal of Computational Design and Engineering, Vol. 6, No. 4, pp. 719-738.
[2] Jian Gao, Xin Chen, Oguzhan Yilmaz and Nabil Gindy (2008), “An integrated adaptive repair solution for complex aerospace components through geometry reconstruction,” International Journal of Advanced Manufacturing Technology, Vol. 36, pp. 1170-1179.
[3] M-Y Lee, C-C Chag and Y.C. Ku (2008), “New layer-based imaging and rapid prototyping techniques for computer-aided design and manufacture of custom dental restoration,” Journal of Medical Engineering and Technology, Vol. 32, No. 1, pp. 83-90.
[4] David W. Rosen (2007), “Computer-aided design for additive manufacturing of cellular structures,” Computer-Aided Design and Applications, Vol. 4, No. 5, pp. 585-594.
[5] Mark J. Clayton, Robert B. Warden, Thomas W. Parker (2002), “Virtual construction of architecture using 3D CAD and simulation,” Automation in Construction, Vol. 11, No. 2, pp. 227-235.
[6] Yusri Yusof and Kamran Latif (2014), “Survey on computer-aided process planning,” International Journal of Advanced Manufacturing Technology, Vol. 75, pp. 77-89.
[7] Mazin Al-wswasi, Atanas Ivanov and Harris Makatsoris (2018), “A survey on smart automated computer-aided process planning (ACAPP) techniques,” International Journal of Advanced Manufacturing Technology, Vol. 97, pp. 809-832.
[8] Xun Xu, Lihui Wang, and Stephen T. Newman (2011), “Computer-aided process planning - A critical review of recent developments and future trends,” International Journal of Computer Integrated Manufacturing, Vol. 24, No. 1, pp. 1-31.
[9] Yang Shi, Yicha Zhang, Kaishu Xia and Ramy Harik (2020), “A critical review of feature recognition techniques,” Computer-Aided Design and Applications, Vol. 17, No. 5, pp. 861-899.
[10] 陳正岳（2022），「以特徵編碼之深度學習進行加工特徵辨識」，碩士論文，臺灣科技大學機械工程系，台北市。
[11] Soori Mohsen and Mohammed Asmael (2021), “Classification of Research and Applications of the Computer Aided Process Planning in Manufacturing Systems,” Independent Journal of Management and Production, Vol. 12, No. 5, pp. 1250–1281.
[12] Xiuling Li, Shusheng Zhang, Rui Huang, Bo Huang, Changhong Xu and Yajun Zhang (2018), “A survey of knowledge representation methods and applications in machining process planning,” International Journal of Advanced Manufacturing Technology, Vol. 98, pp. 3041-3059.
[13] Rui Huang, Junfeng Jiang, Bo Huang and Shusheng Zhang (2019), “Multilevel structured NC machining process model based on dynamic machining feature for process reuse,” International Journal of Advanced Manufacturing Technology, Vol. 104, pp. 2045-2060.
[14] Changhong Xu, Shusheng Zhang, Rui Huang, Bo Huang and Xiuling Li (2016), “NC process reuse-oriented flexible process planning optimization approach for prismatic parts,” International Journal of Advanced Manufacturing Technology, Vol. 87, pp. 329-351.
[15] Kai Tang, Ajay Joneja (2003), “Traversing the machining graph of a pocket,” Computer-Aided Design, Vol. 35, No. 11, pp. 1023-1040.
[16] Kai Tang, Shuo-Yan Chou, Lin-Lin Chen (1998), “An algorithm for reducing tool retractions in zigzag pocket machining,” Computer-Aided Design, Vol. 30, No. 2, pp. 123-129.
[17] Zezhong C. Chen and Qiang Fu (2011), “An optimal approach to multiple tool selection and their numerical control path generation for aggressive rough machining of pockets with free-form boundaries,” Computer-Aided Design, Vol. 43, No. 6, pp. 651-663.
[18] Min Zhou, Guolei Zheng and Zezhong Chevy Chen (2016), “An automated CNC programming approach to machining pocket with complex islands and boundaries by using multiple cutters in hybrid tool path patterns,” International Journal of Advanced Manufacturing Technology, Vol. 83 , pp. 407-420.
[19] 吳佳祐（2023），「以深度學習辨識之加工特徵進行自動化製程規劃」，碩士論文， 臺灣科技大學機械工程系，台北市。