切削刀具磨耗為影響加工業產品精度及良率之重要因素，雖已有諸多研究試圖以模型預估磨耗量，且亦有國際標準組織訂定磨耗機制與標準，然因加工條件之不確定性，仍需監測系統始能有效控制磨耗。現行刀具檢測產品仍未能普及其主因包含「產品無法判斷失效類型」、「無法有效量化磨耗程度」及「檢測過程緩慢」等眾多因素，致使檢測成本過高。且多點刀具因兼具「幾何形狀複雜」、「多磨耗區域」、「旋轉位置不確定」等問題，故難以發展精確、快速且可靠之檢測系統。有鑑於此，本研究利用機械視覺方法開發銑刀磨耗檢測系統，期能達成「重建銑刀三維輪廓」、「辨別刀具測刃的失效型態」及「量化磨耗程度」等至要目標，俾使系統符合實務需求。

The wear of cutter which affects precision and yield of product is one of the important factor. In the past decades, there were many research predict wear by mathematic model. And national organization also define the type and standard of wear. However, due to the processing uncertainty, wear monitoring system is necessary to control wear effectively. So far, the inspective products are not common yet.Those are because of the system inaccuracy and slowly inspective process. Those reasons lead to the high cost of inspection.To develop multipoint cutters inspection system, it should solve “complex geometric shape”, “multiple wear areas”, “spinning position uncertainty” problems. In view of this, this research utilizes machine vision method to develop milling cutter wear inspection system. To match the requirement of practical application, thesis aim to “reconstruct 3D profile of milling cutter”, “quantitative wear level of milling cutter”.

摘要 ii
ABSTRACT iii
致謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1. 前言 1
1.2. 文獻回顧 2
1.2.1. 刀具磨耗機制 2
1.2.2. 刀具檢測產品 5
1.2.3. 刀具檢測技術 10
1.2.4. 非視覺監測系統 12
1.2.5. 視覺類監測系統 15
1.3. 研究動機 20
第二章 理論基礎 21
2.1. 投影重建輪廓技術 21
2.1.1. 凱尼演算法 22
2.1.2. 偏心校正 24
2.1.3. 輪廓重建 26
第三章 實驗設計 28
3.1. 系統評估 28
3.2. 系統架構 29
3.2.1. 系統硬體架構 30
3.2.2. 軟體架構 37
3.2.3. 系統使用流程 37
第四章 實驗結果與討論 39
4.1. 系統解析度測試 40
4.2. 銑刀輪廓重建 41
4.2.1. 刀具影像擷取 41
4.2.2. 影像邊緣偵測 45
4.2.3. 偏心校正 47
4.2.4. 輪廓重建 48
4.2.5. 最小取像張數測試 54
4.3. 系統精度測定 58
4.4. 刀徑量測驗證 59
4.5. 失效及磨耗刀具重建特徵分析 62
4.6. 系統檢測結果與業界人工分類結果比較 72
第五章 結論 75
第六章 未來工作 76
參考文獻 79
附錄一 、待測刀具規格 81
附錄二 、旋轉平台規格 82
附錄三 、驅動器規格 83
附錄四 、光源規格 84
附錄五 、遠心鏡頭之透鏡零件規格 85
附錄六 、光源聚焦透鏡規格 87
附錄七 、相機規格 88
附錄八 、三次元量測儀規格 89
附錄九 、投影重建輪廓程式碼 (Matlab code) 90

1. DeGarmo, E.P., et al., Materials and process in manufacturing. 1997: Prentice Hall.
2. Organization(ISO), I.S., Tool Life Testing in Milling—Part 2: End Milling. 1989: p. 8688-2.
3. Taylor, F.W., On the art of cutting metals. On the Art of Cutting Metals, ASME, 1907. 28.
4. Kuttolamadom, M.A., M.L. Mears, and T.R. Kurfess, On the volumetric assessment of tool wear in machining inserts with complex geometries—part 1: need, methodology, and standardization. Journal of Manufacturing Science and Engineering, 2012. 134(5): p. 051002.
5. Devillez, A., S. Lesko, and W. Mozer, Cutting tool crater wear measurement with white light interferometry. Wear, 2004. 256(1): p. 56-65.
6. Wang, W., Y. Wong, and G. Hong, 3D measurement of crater wear by phase shifting method. Wear, 2006. 261(2): p. 164-171.
7. Prasad, K.N. and B. Ramamoorthy, Tool wear evaluation by stereo vision and prediction by artificial neural network. Journal of Materials Processing Technology, 2001. 112(1): p. 43-52.
8. Hocheng, H., et al., Tool wear monitoring in single-point diamond turning using laser scattering from machined workpiece. Journal of Manufacturing Processes, 2018. 31: p. 405-415.
9. Castejon, M., et al., On-line tool wear monitoring using geometric descriptors from digital images. International Journal of Machine Tools and Manufacture, 2007. 47(12-13): p. 1847-1853.
10. Waydande, P., N. Ambhore, and S. Chinchanikar, A review on tool wear monitoring system. Journal of Mechanical Engineering and Automation, 2016. 6(5A): p. 49-53.
11. Prickett, P. and C. Johns, An overview of approaches to end milling tool monitoring. International Journal of Machine Tools and Manufacture, 1999. 39(1): p. 105-122.
12. Kong, D., Y. Chen, and N. Li, Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models. The International Journal of Advanced Manufacturing Technology, 2017: p. 1-13.
13. Ai, C., et al., The milling tool wear monitoring using the acoustic spectrum. The International Journal of Advanced Manufacturing Technology, 2012. 61(5-8): p. 457-463.
14. Ahmad, M., et al., Development of tool wear machining monitoring using novel statistical analysis method, I-kaz™. Procedia Engineering, 2015. 101: p. 355-362.
15. Malekian, M., S.S. Park, and M.B. Jun, Tool wear monitoring of micro-milling operations. Journal of Materials Processing Technology, 2009. 209(10): p. 4903-4914.
16. Guo, Y. and S. Ammula, Real-time acoustic emission monitoring for surface damage in hard machining. International Journal of Machine Tools and Manufacture, 2005. 45(14): p. 1622-1627.
17. Zhang, C. and J. Zhang, On-line tool wear measurement for ball-end milling cutter based on machine vision. Computers in industry, 2013. 64(6): p. 708-719.
18. Zhu, K. and X. Yu, The monitoring of micro milling tool wear conditions by wear area estimation. Mechanical Systems and Signal Processing, 2017. 93: p. 80-91.
19. Lei, W., et al., Visual Inspection for Breakage of Micro-milling Cutter. Sensors & Transducers, 2014. 182(11): p. 217.
20. Zhang, J., et al., Research on tool wear detection based on machine vision in end milling process. Production Engineering, 2012. 6(4-5): p. 431-437.
21. Szydłowski, M., et al., Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance. Precision Engineering, 2016. 44: p. 236-244.
22. Otsu, N., A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 1979. 9(1): p. 62-66.