電腦 輔助設計

1. 電腦輔助設計 第一單元 曲線(面)

2. 電腦輔助設計

3. 幾何造型 • 三次曲線(cubic spline) • Bezier 曲線(Bezier spline) • B 曲線(B spline)

4. 三次曲線

5. 三次曲線 三次曲線具有下列特點： 1.具有整體控制能力， 沒有局部控制能力因此對於微小的調整較不易掌控。 2.曲線通過所有資料點，所形成的曲線較不圓滑(smooth)。 3.能夠在空間扭曲具有一次反曲點的最低空間曲線。

6. Bezier 曲線

7. Bezier 曲線

8. Bezier曲線是一種以趨近方式來產生曲線， 調整控制點位置，即可控制整個曲線 無法控制局部曲線。 Bezier 曲線

9. B spline 曲線

10. B spline 曲線

11. B spline 曲線的特性： (1). 具有整體與局部控制曲線的能力 (2).Bezier spline 曲線實為B spline 曲線的特例。 (3). 調整控制多邊形上任意點時，最多只會影響到相鄰k個曲線段。 B spline 曲線

12. 線模型(wire model) 線模型(wire model)是利用線條架構的模式來描述三維物體

13. 面模型(surface model) • 面模型(surface model)所描述的物體除了線條與端點的資料 之外，物體在空間表面上任意點的座標點均可充分表達 • 適合表面切削加工

14. 體模型(solid model) • 體模型(solid model)架構除了描述物體在空間表面上任意點資料之外，同時包括內部結構，也就是有厚度的質感 • 適合表面切削加工，快速成型(rapid prototype；RP)

15. 有限元素分析

16. 電腦輔助設計 第二單元 田口實驗法

17. Two examples lead the main layout and name Quality characteristics Dynamicquality characteristics Taguchi Method Overview

18. Case description Strategy無法控制局部曲線。 Case study: Title manufacturing process design

19. Probability distribution fig 1.1.

20. Control factor fig 1.2。

21. Variation level fig 1.3

22. Orthogonal Chart fig 1.4.

23. Experimental equipment fig 1.5

24. Experimental plan fig 1.6.

25. Experimental data fig 1.7

26. Result comparison fig 1.8

27. Case study: brake systemdesign • Case description: • sketch map fig 1.9.

28. I/O break system fig 1.10

30. Control factor and level fig 1.11

31. Experimental plan fig 1.12

32. Experimental data fig 1.13 。

33. Result fig 1.14

34. Chapter 2 Design of Experiments

35. How to arrange experiment? Experiment plans method and orthogonal chart. Interaction, confounding, and resolution Analysis of variance

36. Overview of orthogonal arrays Interaction arrays The quality character on predicting optima design

37. Four methods: trial-and-error one-factor-at-a-time experiments full-factorial experiments Taguchi’s orthogonal arrays experiments The quality character on predicting optima design

38. It depends on one’s experience and sixth sense. It isn’t a systematizing method, and the experience can’t be inherited. Trial-and-error

39. One-factor-at-a-time experiments • One experiment is only change one factor • such as fig 2.1. • An example: plastic injection model deformation • such asfig 2.2. • (1). Factor effect. • (2). Bias. • Main evident defect: • Factor effect is only under some condition.

40. One-factor-at-a-time experiments

41. One-factor-at-a-time experiments

42. Full-factorial experiments • Full consider all combinations of factors. • Main evident defect: • Non efficientAn example: • An example • Basic data and experiment plan: • (1). 4 factors (A, B, C, D) need 24 = 16 experiments fig 2.3. • Experiment data and factor effect fig 2.4. • (1). Response table. • (2). Factor response analysis or analysis of mean, ANOM. • (3). Full-factor reaction figure fig 2.5 and fig 2.6.

43. Full-factorial experiments

44. Full-factorial experiments

45. Full-factorial experiments

46. Full-factorial experiments

47. Concept: Use few experiment to get useful statistic information. Latin squares: La(bc×de) fig 2.7 An example: fig 2.8 and fig 2.9. Make assume: every factor effect is independent and factor effect can be additive Taguchi’s orthogonal arrays experiment method or orthogonal arrays

48. Taguchi’s orthogonal arrays experiment method or orthogonal arrays

49. Taguchi’s orthogonal arrays experiment method or orthogonal arrays

50. The additively of interaction and factor effect