臺灣博碩士論文加值系統

傳統迭代學習控制（ILC）技術為加工提供更好的輸入命令。 但因為大部分商業控制器的限制，我們將命令點位置交由給機器去進行加工，因此我們很難在這些商業控制器上加值應用ILC技術。在本篇論文裡，我們針對XY平面，開發了一種G-code重寫演算法，控制加工路徑來解決這個問題。我們所提出的演算法可以將命令點位置轉換成相對應的G-code指令。

為了保持相同的加工時間，我們需要正確處理進給速率和分段G-code命令的數量。 我們實作所提出的演算法並將其整合至ILC以及LinuxCNC中去進行評估。 對於所產生的G-code，實驗結果證明，我們的演算法搭配ILC可以達到收斂的效果。

The traditional iterative learning control (ILC) technology provides better position commands for the machining. However, most commercial controllers cannot accept position commands to control the machining path directly. Therefore, it is hard to leverage self-developed ILC on these commercial controllers. In this thesis, for XY plane, we develop a G-code rewriting algorithm to control machining path to solve this issue. The proposed algorithm can transfer position commands to the corresponding G-code commands.

In order to preserve the same machining time, we need to properly handle feed rate and the number of segmented G-code commands. We implement the proposed algorithm and integrate it into a customized, ILC-enabled LinuxCNC for the evaluation. For the tested G-code files, the experimental result shows that ILC with the proposed algorithm for contour control can reach a convergence state.

Abstract ii
Content iv
List od figure vii
List of Table xi
1 Introduction 1
1.1 Motivation 2
1.2 Contribution 2
1.3 Related Works 3
1.4 Organization 3
2 Algorithm 4
2.1 Background 4
2.1.1 ILC 4
2.1.2 RMS error 5
2.2 Structure of G-code rewriting algorithm 6
2.3 Design 9
2.3.1 Acceleration Interval 9
2.3.2 Segmentation interval 11
3 Implementation and integration of G-code rewriting algorithm 19
3.1 Overview of LinuxCNC 19
3.2 Implementation 21
3.2.1 Implementation of the acceleration interval 21
3.2.2 Implementation of the segmentation interval 24
4 Experiment 25
4.1 Environment 25
4.1.1 Evaluation configuration 25
4.1.2 Learning-termination condition 29
4.2 Experiment results for straight 29
4.2.1 Convergence ratio: 0.1 29
4.2.2 Convergence ratio: 0.3 31
4.2.3 Convergence ratio: 0.5 33
4.2.4 Convergence ratio: 0.7 35
4.2.5 Convergence ratio: 0.9 38
4.3 Experiment results for Quadrilateral 41
4.3.1 Convergence ratio: 0.1 41
4.3.2 Convergence ratio: 0.3 43
4.3.3 Convergence ratio: 0.5 45
4.3.4 Convergence ratio: 0.7 47
4.3.5 Convergence ratio: 0.9 49
4.3.6 Discussion 52
5 Conclusion 56
5.1 Conclusion 56
5.2 Future work 57

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