跳到主要內容

臺灣博碩士論文加值系統

(44.222.104.206) 您好!臺灣時間:2024/05/28 12:32
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:蔡佳濃
研究生(外文):Chia-Nung Tsai
論文名稱:智慧型三維鈑金夾具設計系統之研究
論文名稱(外文):A Study on Intelligent 3D Panel Fixture Design System
指導教授:林栢村林栢村引用關係
指導教授(外文):Bor-Tsuen Lin
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:機械與自動化工程所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:65
中文關鍵詞:基因遺傳演算法三維鈑金夾具設計參數化設計
外文關鍵詞:Genetic AlgorithmPanel Fixture DesignParametric Design
相關次數:
  • 被引用被引用:0
  • 點閱點閱:269
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本研究的目的是建構智慧型三維鈑金夾具設計系統。三維鈑金夾具設計系統是架構在三維電腦輔助參數設計軟體的參數化實體模型上,應用智慧型全域基因演算法來搜尋各個夾具模組之種類、各項設計變數及固定點方向﹔其目標函數為最簡單的夾具模組與最短的工作行程﹔其拘束條件為夾持機構的夾持力與操作過程中各夾具模組及鈑件的不相互干涉。
在車門鈑金夾具設計中,每組鈑金夾具約需要20個夾具模組,而夾具模組有12個種類,每種夾具模組有2個設計變數,每個夾具模組的固定點需由7個夾持斷面決定其夾持方向,故每組鈑金夾具共有2120個設計變數需同時決定。本研究利用有系統的智慧型全域基因演算法來搜尋一組最佳的設計變數取代目前各廠商慣用且耗時的嘗試錯誤法。該方法利用全域搜尋法則與遺傳基因演算法兩者的優點所結合而成,擁有快速搜尋與強健性等功能,可在極短時間內達到最佳化且每次所得的解相當穩定。特別是用在一些大型複雜的三維鈑金夾具設計,此方法更能表現出比其他傳統最佳化方法優越的性能。運用之演算法包含:實數編碼之基因演算法。所使用的運算子有:選擇演算法、交叉演算法、與突變演算法。
隨後,可將智慧型全域基因演算法與三維電腦輔助設計軟體建構之參數化實體模型技術相整合成智慧型三維鈑金夾具設計系統,並以該系統之設計呈現比現行設計大幅提昇之設計效率及設計品質。
The objective of this project is constructing an intelligent 3D panel fixture design system. The 3D panel fixture design system is based on parametric solid models of 3D CAD software. The intelligent hybrid traditional genetic algorithm (IHTGA) approach is proposed to search the directions of fixed points, type and design variables of each fixture module. The objectives of above approach are the simplest fixture module and shortest fixture stroke. The constraints of above approach include the fixed force of the fixture module and the non-interference among each fixture module and panel at operating processes.
In panel fixture design, each panel fixture system contains about twenty units of fixture modules. The fixture modules are classified into twelve types. Each fixture module includes two design variables. Each clamping position have senven directions to be determined. Hence, a panel fixture system holds 2120 design variables to be resolved simultaneously. However, instead of usually carried out on a time-consuming trial-and-error procedure in most of factory, a systematic reasoning IHTGA approach in this project is proposed to search for the above design variables. The IHTGA approach combines traditional genetic algorithms (TRGA) with Generation of Diverse Offspring by Crossover. Operation genes intelligently to achieve crossover, so that the IHTGA approach possess the merits of global exploration, fast convergence, robustness, and statistical soundness. The performance of the IHTGA approach is better than that of other traditional optimization methods in the large-scale non-linear and non-polynomial systems.
The 3D panel fixture design system, which is integrated IHTGA and parametric solid model of 3D CAD software, is then employed to demonstrate that a revised fixture design can significantly improve design efficiency and quality over an existing design.
目錄
中文摘要…………………………………………………………………i
英文摘要…………………………………………………………………ii
目錄………………………………………………………………………iv
表目錄……………………………………………………………………vi
圖目錄…………………………………………………………………vii
一、緒論…………………………………………………………………1
1.1研究動機……………………………………………………….…1
1.2 研究目的…………………………………………………………2
1.3 文獻回顧…………………………………………………………3
1.4 研究內容…………………………………………………………5

二、三維鈑金夾具模型之建立……………………………………8
2.1 三維設計系統理論基礎…………………………………………8
2.2 鈑金夾具模型之設計理論建立…………………………………11
2.2.1四連桿機構之分……………………………………………13
2.3 三維鈑金夾具幾何模型設計系統之整合………………………16
2.3.1夾具實體模型資料庫………………………………………16
2.3.2三維鈑金夾具模型設計系統之整合………………………18
三、三維鈑金夾具設計之最佳化………………………………………20
3.1 前言………………………………………………………………20
3.2 夾具最佳化設計方法應用………………………………………21
3.2.1基因演算法…………………………………………………22
3.2.2懲罰函數法…………………………………………………25
3.3 三維鈑金夾具最佳化之設計……………………………………26
四、智慧型三維鈑金夾具設計…………………………………………31
4.1 智慧型夾具設計之系統架構……………………………………31
4.2 智慧型三維鈑金夾具設計方法…………………………………32
五、智慧型三維鈑金夾具設計實做 -以車門夾具為例………………51
5.1 前言………………………………………………………………51
5.2 車門夾具最佳化的設計模型……………………………………51
5.3 車門夾具設計之智慧型全域基因演算…………………………55
5.4 實驗結果…………………………………………………………57
5.4.1 GA運算……………………………………………………..57
5.4.2結果驗證……………………………………………………58
5.4.3效果比較……………………………………………………61
六、結論與建議…………………………………………………………62
6.1 研究結論…………………………………………………………62
6.2 未來研究方向之建議……………………………………………63
七、參考文獻……………………………………………………………64
(1)Darvishi, A.R., and K. F. Gill,“Knowledge Representation Database for Development of a Fixture Design Expert. System,”Proceedings of the Institution of Mechanical Engineerings, Vol.202, No.B 1, 1988,PP. 37-39.
(2)Miller, R. S., and R. G. Hannam,‘Cornputer Aided Design Using a Knowledge Base Approach and Its Applicatxons to The Design of Jigs and Ftxtures,” Proceedings of the Institution of Mechanical Englneerzng Vo1.199 No B1 1985 pp.227-234.
(3)Grippo, R M., M. V. Grandhi, and B. S. Tompson, “The Computer Aided Design of Modular Fixmring System,” the International journal of Advanced Manufacturing Technology, Vol.2, No.2, 1987 pp 75-88.
(4)Boerma, J R., and H .I j. Kals, ’Fixture Design with FIXFS Automatic Selection of Position, Clamping and Support Feature for Prismatic Parts,” Annals of the CIRP, Vol.38/1/1989, pp.399-402.
(5)Nee, A Y. C., and A S Kumer, A Feature-Based Classification S,cheme for Fixtures” ,Annals of the CIRP Vo141/1/1992, pp. 189-192.
(6)Nee, A Y. C., and A. S. Kumer, “A Framework for an Object/Rule-Based Automated Fixture Design System,” Annals of the CIRP, Vol. 140/1/1991, pp.147-151.
(7)Dong, X., W. R. Devries, and M. J. Wozny, “Feature-Based Reasoning in Fixture Design,” Annals of the CIRP, Vol.40/1/1991 pp.ll 1-114.
(8)Leung, Y. W. and Y. Wang, 2001, “An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization”, IEEE Trans. on Evolutionary Computation, Vol. 5, pp. 41-53.
(9)Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Massachusetts.
(10)Gen, M. and Cheng, R.W., (1997), “Genetic Algroithms and Engineering Design”, Wiley, New York.
(11)Renders, J. and H. Bersini, 1994, “Hybridizing Genetic Algorithms with Hill-Climbing Methods for Global Optimization: Two Possible Ways”, Proc. of the First IEEE Conf. on Evolutionary Computation, Florida, pp. 312-317.
(12)Michalewicz, Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin.
(13)Chen, C. L., V. S. Vempati, and N. Aljaber, 1995, “An Application of Genetic Algorithms for Flow Shop Problem”, European Journal of Operational Research, Vol. 80, pp. 389-396.
(14)Yen, J. and B. Lee, 1997, “A Simplex Genetic Algorithm Hybrid”, Proc. of the IEEE Int. Conf. on Evolutionary Computation, Indiana, pp. 175-180.
(15)Wang, L., 2001, Intelligent Optimization Algorithms with Applications, Tsinghua University Press, Beijing.
(16)Angelov, P., 2001, “Supplementary Crossover Operator for Genetic Algorithms Based on the Center-of-Gravity Paradigm”, Control and Cybernetics, Vol. 30, pp. 159-176.
(17)Aggoune, R., A. H. Mahdi, M. C. Portmann, 2001, “Genetic Algorithms for the Flow Shop Scheduling Problem with Availability Constraints”, 2001 IEEE International Conference on Systems, Man, and Cybernetics, Tucson, Arizona, Vol. 4, pp. 2546 –2551.
(18)Zhang, G., W. Jin, and F. Jin, 2003, “Multi-criterion Satisfactory Optimization Method for Designing IIR Digital Filters”, Proceeding of ICCT 2003, Beijing, pp. 1484-1490.
(19)Tsai, J. T., T. K. Liu, and J. H. Chou, 2003, “Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization”, IEEE Trans. (Paper No.: TEC #731RR).
(20)周明,孫樹棟,1998,遺傳算法原理及應用,國防工業出版社,北京。.
(21)T. K. Liu, “Application of Multi-objective Genetic Algorithms to Control System Design”, Doctorial Thesis of Graduate School of Sciences, Tohoku University, Japan, 1997.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top