請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90082
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔡曜陽 | zh_TW |
dc.contributor.advisor | Yao-Yang Tsai | en |
dc.contributor.author | 蔡兆庭 | zh_TW |
dc.contributor.author | Zhao-Ting Tsai | en |
dc.date.accessioned | 2023-09-22T17:20:12Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-11 | - |
dc.identifier.citation | [1] P. Lee and Y. Altintaş, "Prediction of ball-end milling forces from orthogonal cutting data," International Journal of Machine Tools and Manufacture, vol. 36, no. 9, pp. 1059-1072, 1996.
[2] L. López de Lacalle*, A. Lamikiz, J. Muñoa, and J. Sánchez, "The CAM as the centre of gravity of the five-axis high speed milling of complex parts," International Journal of Production Research, vol. 43, no. 10, pp. 1983-1999, 2005. [3] E. Budak and Y. Altintas, "Analytical prediction of chatter stability in milling—part I: general formulation," 1998. [4] Y. Boz, H. Erdim, and I. Lazoglu, "A comparison of solid model and three-orthogonal dexelfield methods for cutter-workpiece engagement calculations in three-and five-axis virtual milling," The International Journal of Advanced Manufacturing Technology, vol. 81, pp. 811-823, 2015. [5] Y. Yang, W. Zhang, M. Wan, and Y. Ma, "A solid trimming method to extract cutter–workpiece engagement maps for multi-axis milling," The International Journal of Advanced Manufacturing Technology, vol. 68, pp. 2801-2813, 2013. [6] Q. Guo, Y. Sun, Y. Jiang, Y. Yan, B. Zhao, and P. Ming, "Tool path optimization for five-axis flank milling with cutter runout effect using the theory of envelope surface based on CL data for general tools," Journal of Manufacturing Systems, vol. 38, pp. 87-97, 2016. [7] C.-S. Jun, K. Cha, and Y.-S. Lee, "Optimizing tool orientations for 5-axis machining by configuration-space search method," Computer-Aided Design, vol. 35, no. 6, pp. 549-566, 2003. [8] Z. Mi, C.-M. Yuan, X. Ma, and L.-Y. Shen, "Tool orientation optimization for 5-axis machining with C-space method," The International Journal of Advanced Manufacturing Technology, vol. 88, pp. 1243-1255, 2017. [9] Z. Gong, B. Li, H. Zhang, and P. Ye, "Tool orientation optimization method based on ruled surface using genetic algorithm," The International Journal of Advanced Manufacturing Technology, pp. 1-14, 2022. [10] S. A. Abbasi, P. Feng, Y. Ma, X. Cai, D. Yu, and Z. Wu, "Influence of tool inclination angle and cutting direction on long thin-walled part’s dimensional and geometric accuracy when high-speed ball end milling the heat-treated titanium alloy Ti–6Al–4 V," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 230, no. 15, pp. 2676-2698, 2016. [11] T. Huang, X.-M. Zhang, and H. Ding, "Tool orientation optimization for reduction of vibration and deformation in ball-end milling of thin-walled impeller blades," Procedia CIRP, vol. 58, pp. 210-215, 2017. [12] A. Zhang, C. Yue, X. Liu, and S. Y. Liang, "Study on the Formation Mechanism of Surface Adhered Damage in Ball-End Milling Ti6Al4V," Materials, vol. 14, no. 23, p. 7143, 2021. [13] J. A. García-Barbosa, J. M. Arroyo-Osorio, and E. Córdoba-Nieto, "Influence of tool inclination on chip formation process and roughness response in ball-end milling of freeform surfaces on Ti-6Al-4V alloy," Machining Science and Technology, vol. 21, no. 1, pp. 121-135, 2017. [14] F. Liang, C. Kang, and F. Fang, "A review on tool orientation planning in multi-axis machining," International Journal of Production Research, vol. 59, no. 18, pp. 5690-5720, 2021. [15] R. A. Mali, T. Gupta, and J. Ramkumar, "A comprehensive review of free-form surface milling–Advances over a decade," Journal of Manufacturing Processes, vol. 62, pp. 132-167, 2021. [16] I. Zeid, CAD/CAM Theory and Practice. McGraw-Hill, 1991. [17] 吳正傑, "加工特徵辨識應用於三軸之加工規劃," 國立臺灣大學. [18] D.-B. Perng, Z. Chen, and R.-K. Li, "Automatic 3D machining feature extraction from 3D CSG solid input," Computer-Aided Design, vol. 22, no. 5, pp. 285-295, 1990. [19] E. Ozturk and E. Budak, "Modeling of 5-axis milling processes," Machining science and technology, vol. 11, no. 3, pp. 287-311, 2007. [20] S. Engin and Y. Altintas, "Mechanics and dynamics of general milling cutters.: Part I: helical end mills," International journal of machine tools and manufacture, vol. 41, no. 15, pp. 2195-2212, 2001. [21] E. Ozturk, L. T. Tunc, and E. Budak, "Investigation of lead and tilt angle effects in 5-axis ball-end milling processes," International Journal of Machine Tools and Manufacture, vol. 49, no. 14, pp. 1053-1062, 2009. [22] I. Lazoglu, "A new identification method of specific cutting coefficients for ball end milling," Procedia Cirp, vol. 14, pp. 182-187, 2014. [23] 黃顯雄, "應用切削力分析於五軸加工路徑優化," 國立臺灣大學, 2022. [24] G. C. Loney and T. M. Ozsoy, "NC machining of free form surfaces," Computer-Aided Design, vol. 19, no. 2, pp. 85-90, 1987. [25] 洪. 李家岩, 製造數據科學:邁向智慧製造與數位決策. 前程文化, 2022. [26] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, 1995, vol. 4: IEEE, pp. 1942-1948. [27] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), 1998: IEEE, pp. 69-73. [28] N. Yusup, A. M. Zain, and S. Z. M. Hashim, "Overview of PSO for optimizing process parameters of machining," Procedia Engineering, vol. 29, pp. 914-923, 2012. [29] Z. Wang, X. Lin, Y. Shi, Y. Zhang, and Z. Chen, "Reducing roughness of freeform surface through tool orientation optimization in multi-axis polishing of blisk," The International Journal of Advanced Manufacturing Technology, vol. 108, pp. 917-929, 2020. [30] R. C. Eberhart and Y. Shi, "Tracking and optimizing dynamic systems with particle swarms," in Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), 2001, vol. 1: IEEE, pp. 94-100. [31] J. Xin, G. Chen, and Y. Hai, "A particle swarm optimizer with multi-stage linearly-decreasing inertia weight," in 2009 International joint conference on computational sciences and optimization, 2009, vol. 1: IEEE, pp. 505-508. [32] A. Nikabadi and M. Ebadzadeh, "Particle swarm optimization algorithms with adaptive Inertia Weight: A survey of the state of the art and a Novel method," IEEE journal of evolutionary computation, 2008. [33] Y. Feng, G.-F. Teng, A.-X. Wang, and Y.-M. Yao, "Chaotic inertia weight in particle swarm optimization," in Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 2007: IEEE, pp. 475-475. [34] M. S. Arumugam and M. Rao, "On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems," Discrete Dynamics in Nature and Society, vol. 2006, 2006. [35] W. Al-Hassan, M. Fayek, and S. Shaheen, "Psosa: An optimized particle swarm technique for solving the urban planning problem," in 2006 international conference on computer engineering and systems, 2006: IEEE, pp. 401-405. [36] 陳國禎, "應用粒子群演算法與搜尋策略於剪力構架之結構勁度參數修正," 國立交通大學, 2018. [37] 洪櫧鈞, "基於八元樹法之五軸切削力及切削彎矩估測," 國立臺灣大學, 2022. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90082 | - |
dc.description.abstract | 隨著數位控制銑床工具機的進步,自由曲面刀具路徑優化及切削力之控制已經成為了現代高精度加工領域中一項極具挑戰的問題。如何有效地進行刀具路徑優化,確保刀具的適切角度、降低加工時間、提高產品品質是目前加工製造業邁向智慧化的重要議題。對於刀具路徑而言,尋找最佳刀具方向(Tool orientation),將有助於降低切削力、減少刀具磨損、提升產品的品質及加工效率。然而,路徑優化是非線性且高度受制約的最佳化問題,傳統的最佳化方法往往難以獲得理想的結果。
針對上述問題,本研究將提出一種基於粒子群最佳化(Particle Swarm Optimization,PSO)演算法的刀具路徑優化方法。利用 PSO 的全局搜尋能力和適合解決連續性最佳化問題之特點,通過建立加工過程中的切削力模型,結合圖形幾何計算和刀具路徑生成技術,針對銑削自由曲面過程中之刀具軸向進行優化。並將刀具角度優化問題轉化為一個多目標優化問題,經優化後可得低切削力且刀具軸向變動小之刀具路徑。 驗證結果顯示,本研究所提出的基於 PSO 演算法的刀具路徑優化方法,可以有效地使加工路徑平滑化及減少切削力。此方法也具有廣泛的應用前景,可適用於各種複雜形狀的自由曲面銑削。 | zh_TW |
dc.description.abstract | With the advancement of digital control milling machine tools, the optimization of free-form tool paths and the control of cutting force have become a highly challenging problem in the field of modern high-precision machining. How to effectively optimize tool paths, ensure proper tool angles, reduce machining time, and improve product quality is an important topic as the manufacturing industry moves towards intelligence. In terms of tool paths, finding the optimal tool orientation can help reduce cutting forces, decrease tool wear, improve product quality, and increase machining efficiency. However, path optimization is a nonlinear and highly constrained optimization problem, and traditional optimization methods often struggle to achieve ideal results.
In response to these issues, this study proposes a tool path optimization method based on the Particle Swarm Optimization (PSO) algorithm. By utilizing the global search capability of PSO and its suitability for solving continuous optimization problems, a cutting force model is established during the machining process. Combined with graphic geometric calculation and tool path generation techniques, the tool axis in the milling process of free surfaces is optimized. The tool angle optimization problem is transformed into a multi-objective optimization problem, and after optimization, a tool path with low cutting force and small tool axis variation can be obtained. Validation results show that the tool path optimization method based on the PSO algorithm proposed in this study can effectively smooth the machining path and reduce cutting forces. This method also has a wide range of application prospects and can be applied to the milling of various complex-shaped free surfaces. In the future, it will have practical value in actual production. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:20:12Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-09-22T17:20:12Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 目錄
誌謝 i 摘要 ii Abstract iii 圖目錄 vii 表目錄 x 第1章 緒論 1 1.1 研究背景 1 1.2 文獻回顧 3 1.3 研究動機和目的 5 1.4 論文大綱 7 第2章 理論模型建立 8 2.1 CAD模型 8 2.1.1 CAD形體模型 8 2.1.2 圖檔格式說明 16 2.2 刀具形貌 19 2.2.1 球型銑刀之幾何 19 2.2.2 球型銑刀之輪廓幾何 19 2.3 空間座標系 23 2.3.1 機械座標及轉換 23 2.4 刀具切削座標系 28 2.4.1 刀具軸向定義 28 2.4.2 切削力模型 30 2.5 加工誤差 34 2.5.1 扇形高度 34 2.5.2 弦高誤差 34 第3章 切削模擬演算法 36 3.1 流程架構 36 3.2 幾何擷取方法 37 3.2.1 CLSF介紹 37 3.2.2 切削模擬演算法 41 3.3 刀具軸向設置 43 3.3.1 刀具軸向 43 3.3.2 刀具軸向限制邊界 44 第4章 粒子群優化切削力演算法 46 4.1 最佳化算法介紹 46 4.1.1 粒子群演算法簡介 48 4.1.2 PSO慣性權重 52 4.1.3 PSO權重因子優化 53 4.1.4 學習因子 56 4.1.5 刀具軸向優化問題之學習因子設置 56 4.2 最佳化目標函數與限制函數 59 4.2.1 最佳化演算法流程 59 4.2.2 切削力計算 60 4.2.3 目標函數設置 61 4.2.4 限制函數設置 61 4.2.5 刀具軸向最佳化演算法之適應度函數 62 第5章 程式實作與討論 64 5.1 程式規劃設計 64 5.1.1 開發環境與使用軟體 64 5.1.2 程式實作流程 64 5.2 程式實作結果與驗證 65 5.2.1 切削力參數設置 68 5.2.2 PSO最佳化參數設置 70 5.2.3 執行刀具軸向優化 70 5.3 結果與討論 72 5.3.1 模擬結果 72 5.3.2 模擬結果延伸探討 75 第6章 結論與未來展望 79 6.1 結論 79 6.2 未來展望 80 參考文獻 81 | - |
dc.language.iso | zh_TW | - |
dc.title | 基於粒子群演算法之五軸加工刀具軸向優化 | zh_TW |
dc.title | Optimization of Tool Orientation Based on Particle Swarm Algorithm in Five-Axis Milling | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 李貫銘;盧銘詮 | zh_TW |
dc.contributor.oralexamcommittee | Kuan-Ming Li;Ming-Chyuan Lu | en |
dc.subject.keyword | 加工路徑生成,刀具軸向,切削力,加工嚙合區域,粒子群演算法,進給率, | zh_TW |
dc.subject.keyword | Path generation,Tool orientation,Cutting force,CWE,PSO,Feed rate, | en |
dc.relation.page | 83 | - |
dc.identifier.doi | 10.6342/NTU202304135 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-13 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 機械工程學系 | - |
顯示於系所單位: | 機械工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 3.87 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。