臺灣博碩士論文加值系統

現今工業都朝向自動化發展，各家工具機廠商亦致力於提升產品的品質及自動化生產的目標，刀具磨耗的情況常常會對產品的品質與自動化生產的效率產生重大影響。針對銑床的端銑刀磨耗檢測的需求，本研究希望研發一套機械視覺為基礎的線上刀具磨耗檢測方法。由於端銑刀的複數刀刃與螺旋形刀具形狀不同於一般車刀，加上金屬的反光的效應，增加的端銑刀在影像拍攝困難，本研究利用刀具影像疊圖的技術將立體的端銑刀展開成平面圖面，並使用影像處理的方法對展開後的刀具圖片進行磨耗檢測。實際以工具機機台進行切削加工，定時進行刀具影像疊圖並檢測刀具磨耗狀態，可驗證本研究發展機械視覺系統應用銑床的端銑刀於線上刀具磨耗檢測的可行性。

In recent years, the industry is developing towards automation. Various machine tool manufacturers have devoted to improving the quality of products and reaching the goal of automated production. Tool wear has a significant impact to the quality of products and automated production. Because the demand of automatic online inspection system of tool wear increases, this study try to develop a method for automatic online inspection system of tool wear based on machine vision. Since the number of teeth and the geometric shape of the end mills are different from the turning tool, end mills are more difficult to shoot in images. This study develops a method of tool stitching to expand the three-dimensional end mill into a plan view and uses the image processing method to perform wear detection on the expanded tool image. The experiment results based on the practical milling in machine tool monitor the tool wear condition at regular time. The thesis verifies the feasibility that machine vision system can be applied on the automatic online inspection of end mill.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
符號表 IX
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 1
1.2.1 間接式檢測 1
1.2.2 直接式檢測 2
1.3 論文架構 2
第二章 實驗設備介紹 3
2.1 影像擷取設備 3
2.2 照明系統 5
第三章 刀具疊圖技術 7
3.1 疊圖技術 7
3.1.1 使用SIFT對刀具進行展開 7
3.1.2 使用Template matching對刀具進行展開 9
第四章 磨耗檢測 11
4.1 磨耗定義 11
4.2 影像處理 13
4.2.1 影像處理流程 13
4.2.2 使用Canny edge detection檢測刀具邊緣 14
4.2.3 使用 Hough Transform檢測刀刃邊緣 17
4.2.4 使用Otsu's method分割出磨耗區域 18
4.2.5 使用Opening與Closing對磨耗區域型態處理 20
4.2.6 磨耗點位置檢測與計算 23
第五章 實驗結果與數據分析 25
5.1 切削實驗 25
5.2 磨耗檢測 27
第六章 結論與未來方向 30
6.1 結論 30
6.2 未來方向 30
參考文獻 31

[1]ISO 8688-2, “Tool life testing in milling — Part 2 End milling”, 1989.
[2]E. Dimla, and S. Dimla, “Sensor signals for Tool-wear Monitoring in Metal Cutting Operations—A Review of Methods,” International Journal of Machine Tools and Manufacture, vol. 40, pp. 1073–1098, 2000.
[3]S. Kurada and C. Bradley, “A review of machine vision sensors for tool condition monitoring”, Computers in Industry, vol. 34, pp. 55–72, 1997.
[4]K. Khalili and M. Danesh, “Effect of Cutting Tool Geometry on Morphology of Flank Wear Land in Turning of Low Carbon Steels” Research Journal of Applied Sciences, Engineering and Technology, vol. 6, no. 20, pp. 3798-3807, 2013.
[5]J. Yu, “Machine Tool Condition Monitoring Based on an Adaptive Gaussian Mixture Model,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 134, no. 3, pp. 031004-(1-13), 2012.
[6]C. Scheffer and P.S. Heyns, “Monitoring of Turning Tool Wear Using Vibration Measurements and Neural Network Classification,” Proc. Proceedings of the 25th International Conference on Noise and Vibration Engineering, ISMA, pp. 921-928, 2000.
[7]J. Wilkowski and J. Górski, “Vibro-Acoustic Signals As A Source Of Information About Tool Wear During Laminated Chipboard Milling,” Wood Research , vol. 6 no. 1, pp.57-66, 2004,
[8]Y. Kwon, Y. Ertekin and T.L. Tseng, “Characterization of Tool Wear Measurement With Relation to The Surface Roughness in Turning,” Machining Science and Technology, vol. 8, no. 1 pp. 39-51, 2004.
[9]S. Dutta, A. Datta, N.D. Chakladar, S.K. Pal, S. Mukhopadhyay and R. Sen, “Detection of Tool Condition From The Turned Surface Images Using An Accurate Grey Level Cooccurrence Technique,” Precision Engineering, vol. 36, no. 3, pp. 458-466.
[10]C. Bradley and Y.S. Wong, “Surface Texture Indicators of Tool Wear – A Machine Vision. Approach,” International Journal of Advanced Manufacturing Technology, vol. 17, no. 6, pp. 435-443, 2001.
[11]B.S. Prasad, K.A. Prabha and P.V.S.G. Kumar, “Condition Monitoring of Turning Process Using Infrared Thermography Technique – An experimental approach,” Infrared Physics and Technology, vol. 81, pp. 137-147, 2017.
[12]S. Smith, B. Woody, W. Barkman and D. Tursky, “Temperature Control and Machine Dynamics in Chip Breaking Using CNC Toolpaths,” CIRP Annals - Manufacturing Technology, vol. 58, no. 1, pp. 97-100, 2009.
[13]R .F. Hamade and A.H. Ammouri, “Current Rise Index (CRI) Maps of Machine Tool Motors for Tool-wear Prognostic,” Proc. ASME 2011 International Mechanical Engineering Congress and Exposition, pp. 867-872, 2011.
[14]W. H. Wang, Y.S. Wong and G.S. Hong, “3D Measurement of Crater Wear by Phase Shifting Method,” Wear, vol. 261, no. 2, pp. 164-171, 2006.
[15]T. G. Dawson and T.R. Kurfess, “Quantification of Tool Wear Using White Light Interferometry and Three-dimensional Computational Metrology,” International Journal of Machine Tools and Manufacture, vol. 45, no. 4-5, pp. 591-596, 2005.
[16]B.S. Prasad, M.M.M. Sarcar and B.S. Ben, “Surface Textural Analysis Using Acousto Optic Emission and Vision-based 3D Surface Topography A Base for Online Tool Condition Monitoring in Face Turning,” International Journal of Advanced Manufacturing Technology, vol. 55, no. 9-12, pp. 1025-1035, 2011.
[17]F. Giusti, M. Santochi and G. Tantussi, “A Flexible Tool Wear Sensor for NC Lathes,” CIRP Annals - Manufacturing Technology, vol. 33, no. 1, pp. 229-232, 1984.
[18]S. Kurada and C. Bradley, “A Machine Vision System for Tool Wear Assessment,” Tribology International, vol. 30, no. 4, pp. 295-304, 1997.
[19]Q. Yuan, S.M. Ji and L. Zhang, “Study of Monitoring the Abrasion of Metal Cutting Tools Based on Digital Image Technology,” Proc. Proceedings of SPIE - The International Society for Optical Engineering, pp. 397-402, 2004.
[20]P. Y. Li, Y. Li, J.M. Zheng, D. Zhang and C.Y. Hao, “Tool Cutting Edge Line Detection Based on Improved Hough Transform,” Book Tool cutting edge line detection based on improved Hough transform, Series Tool cutting edge line detection based on improved Hough transform 455, pp. 59-65, 2011.
[21]F. Zhao, Q. Huang, W. Gao. “Image matching by Normalized Cross Correlation, ” IEEE Acoustics, Speech & Signal Processing ,Vol. 2, pp. 729-732, 2006.
[22]M. Brown and D. Lowe. “Automatic Panoramic Image Stitching using Invariant Features,” International Journal of Computer Vision, Vol. 74, pp. 59-73, 2007.
[23]R. Szeliski. “Computer Vision: Algorithms and Applications”. Springer, New York, 2010.
[24]R. Szeliski. “Image alignment and stitching: A tutorial.” Technical Report MSR-TR-2004-92, Microsoft Research, December 2004.
[25]T. W. Yeh, “Turning Tool Monitoring using Machine Vision,” Unpublished Master’s thesis, National Cheng Kung University, R.O.C. 2017.
[26]L. G. David, “Object recognition from local scale-invariant features,” Proceedings of the International Conference on Computer Vision 2. pp. 1150-1157.doi:10.1109/ICCV. 790410. 1999.
[27]R. Gonzalez and R. Woods. Digital Image Processing. Addison-Wesley, 2nd edition, 1993.
[28]J. Canny. “A Computational Approach to Edge Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, no. 16, pp. 679-698, 1986.
[29]J. Matas and C. Galambos and J.V. Kittler, “Robust Detection of Lines Using the Progressive Probabilistic Hough Transform,” CVIU 78, pp 119-137, 2000.
[30]M. Sezgin and B. Sankur. “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol.13, no. 1, pp. 146–165, 2004.
[31]O, Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys., Man., Cyber, vol. 9, no. 1, pp. 62–66. 1979.