臺灣博碩士論文加值系統

在品質要求的年代，切削依然是製造業裡重要的一環，想要呈現最好的工件，勢必跟切削刀具有密切的關係。本論文採用銑床中常用的端銑刀來做檢測並且搭配偏擺檢查儀來夾持刀具。本文以視覺檢測技術方面藉非接觸式量測去檢測端銑刀來建構端銑刀量測系統，避免端銑刀表面產生刮痕。此研究是藉由工業相機量測端銑刀的直徑與螺旋角等角度與切削後的磨損，並將量測結果顯示在PC端的系統介面上，並且搭配資料庫儲存資料。可根據各種不同的產業需求，來搭配不同尺寸的刀具，端銑刀刀刃數也可以自由挑選，應用範圍廣泛。量測系統中檢測直徑12 mm的三刃端銑刀，夾持端選用偏擺檢查儀來夾持端銑刀。視覺方面使用兩個相同工業相機，相機分別裝設兩顆焦距50 mm 及75 mm CCTV鏡頭及搭配外同軸光源、背光光源。兩個工業相機解析度為1920 pixel × 1200 pixel，視野範圍(FOV)分別為57 mm × 36 mm及13 mm × 8 mm。本系統直徑重複性平均為10 µm內。螺旋角、刀傾角、軸向離隙角與軸向餘隙角、徑向餘隙角與餘隙角的重複性各別為12角分，2角分，4角分，15角分，23角分及41角分。

In the age of quality requirements, cutting is still an important part of the manufacturing industry. If present the best workpiece, it is bound to have a close relationship with the cutting tool. In this paper, the end mill cutter commonly used in milling machines are used for inspection, and the tool is clamped with runout measuring machine. This article uses visual inspection technology, a non-contact measurement instrument is used to inspect the end mill cutter and also construct system for the end mill cutter measuring. The end mill cutter measurement system is constructed to avoid scratches on the surface of the end mill cutter. This research uses industrial cameras to measure the diameter and helix angle of the end mill cutter and the wear after cutting and displays the measurement results on the system’s interface on the PC side, and stores data in the database. It can be matched with different sizes of tools according to various industrial needs, and the number of flute of end mills cutter can be freely selected, which has a wide range of applications. The measuring system detects a three-flute end mill with a diameter of 12mm and the clamping end uses the runout measuring machine to clamp the end mill cutter. Visually use two identical industrial cameras. The industrial camera is equipped with two CCTV lenses with focal lengths of 50 mm and 75 mm, with coaxial light sources and back light sources. The resolution of the two industrial cameras is 1920 pixel × 1200 pixel, and the field of view (FOV) is 57 mm × 36 mm and 13 mm × 8 mm. The outer diameter repeatability of this system is within 10 µm. The measurements repeatability of helix angle, rake angle, axial clearance angle and axial relief angle, radial clearance angle and relief angle is within 12, 2, 4, 15, 23 and 41 arc-min.

摘要......i
Abstract......ii
誌謝......iv
目錄......v
表目錄......viii
圖目錄......xi
第一章 緒論......1
1.1 前言......1
1.2 研究目的......3
1.3 國內外相關文獻......4
1.4 論文架構......16
第二章 系統介紹......17
2.1 硬體架構......17
2.2 系統架構......18
2.3 設備元件架構-光源端......20
2.3.1 Ardunio UNO板......20
2.3.2 LED調光電源供應器......21
2.3.3 LED 外同軸光源......23
2.3.4 LED背光板......24
2.4 設備元件架構-視覺端......25
2.4.1 工業相機......26
2.4.2 鏡頭......27
2.5 設備元件架構-夾持端......28
2.5.1 偏擺檢查儀......28
2.5.2 刀把規格......29
2.5.3 端銑刀......30
2.5.4 筒夾......31
第三章 端銑刀影像量測方法......32
3.1 視覺檢測演算......32
3.1.1 影像處理......32
3.1.2 影像邊緣讀取......34
3.1.3 模板匹配......37
3.1.4 傅立葉變換......40
3.2 端銑刀量測原理......43
3.2.1 直徑量測原理......43
3.2.2 離隙角與餘隙角量測原理......46
3.2.3 端銑刀旋轉角度量測......47
3.2.4 側邊磨損量測原理......50
3.2.5 端面磨損量測原理......51
第四章 實驗結果與討論......52
4.1 人機介面 (Human-Machine Interface, HMI)......52
4.1.1 端銑刀側邊檢視頁面......53
4.1.2 端銑刀端面檢視頁面......56
4.1.3 相機資訊......58
4.1.4 數據顯示......59
4.2 表面粗糙度量測......62
4.2.1 SJ-410各部位介紹......63
4.2.2 量測實際架設圖......64
4.2.3 切削路徑與NC-Code......66
4.2.4 表面粗糙度[39]......67
4.2.5 切削對照......68
4.2.6 實驗流程......69
4.2.7 實驗結果......70
4.3 端銑刀實驗結果(未切削)......76
4.3.1 刀具設定儀......76
4.3.2 直徑量測......78
4.3.3 軸向離隙角、餘隙角以及刀傾角量測......100
4.3.4 徑向離隙角、餘隙角量測......120
4.3.5 端銑刀量測系統量測結果彙整......129
4.4 實驗結果討論......133
4.5 不確定因素分析......135
第五章 結論與未來展望......137
5.1 結論......137
5.2 未來展望......138
參考文獻......139
Extended Abstract......143

[1]廖家鉦，端銑刀自動化量測之研究，碩士論文，機械工程學系研究所，台灣，2011。
[2]林祺勛，五軸工具磨床線上刀具幾何影像檢測系統開發，碩士論文，國立虎尾科技大學創意工程與精密科技研究所，台灣，2011。
[3]蘇聰，端銑刀臨界磨耗之線上即時監控方法研究，碩士論文，中原大學機械工程研究所，台灣，2011。
[4]Rejc, J., Kovačič, F., Trpin, A., Turk, I., Štrus, M., Rejc, D., Munih, M. (2011). The mechanical assembly dimensional measurements with the automated visual inspection system. Expert Systems with Applications, 38(8), 10665-10675.
[5]N.S.S. Mar, P.K.D.V. Yarlagadda, C. Fookes. (2011). Design and development of automatic visual inspection system for PCB manufacturing.School of Engineering Systems, Queensland University of Technology, George Street 2, Brisbane QLD 4001, Australia.
[6]陳正和、黃彥翔，機器視覺系統運用於刀片磨耗研究，綠色科技工程與應用研討會(GTEA)，國立勤益科技大學機械工程系研究所，台灣，2013。
[7]Zhang, C., & Zhang, J. (2013). On-line tool wear measurement for ball-end milling cutter based on machine vision. Computers in Industry, 64(6), 708-719.
[8]Shanmugamani, R., Sadique, M., & Ramamoorthy, B. (2015). Detection and classification of surface defects of gun barrels using computer vision and machine learning. Measurement, 60, 222-230.
[9]Manish, R., Venkatesh, A., & Denis Ashok, S. (2018). Machine Vision Based Image Processing Techniques for Surface Finish and Defect Inspection in a Grinding Process. Materials Today: Proceedings, 5(5), 12792-12802.
[10]陳正益，端銑刀磨耗線上量測，碩士論文，國立中正大學工學院機械工程學系研究所，台灣，2018。
[11]黃韻喬，端銑刀刃口影像檢測系統開發，碩士論文，國立虎尾科技大學機械與電腦輔助工程系碩士班，台灣，2018。
[12]Matic, T., Aleksi, I., Hocenski, Z., & Kraus, D. (2018). Real-time biscuit tile image segmentation method based on edge detection. ISA Trans, 76, 246-254.
[13]Agic, A., Eynian, M., Ståhl, J. E., & Beno, T. (2019). Experimental analysis of cutting edge effects on vibrations in end milling. CIRP Journal of Manufacturing Science and Technology, 24, 66-74.
[14]Alhadeff, L. L., Marshall, M. B., Curtis, D. T., & Slatter, T. (2019). Protocol for tool wear measurement in micro-milling. Wear, 420-421, 54-67.
[15]Dai, Y., & Zhu, K. (2018). A machine vision system for micro-milling tool condition monitoring. Precision Engineering, 52, 183-191.
[16]Aimin, Y., Shanshan, L., Honglei, L., & Donghao, J. (2018). Edge extraction of mineralogical phase based on fractal theory. Chaos, Solitons & Fractals, 117, 215-221.
[17]Manish, R., Venkatesh, A., & Denis Ashok, S. (2018). Machine Vision Based Image Processing Techniques for Surface Finish and Defect Inspection in a Grinding Process. Materials Today: Proceedings, 5(5), 12792-12802.
[18]Mendikute, A., Leizea, I., Yagüe-Fabra, J. A., & Zatarain, M. (2018). Self-calibration technique for on-machine spindle-mounted vision systems. Measurement, 113, 71-81.
[19]Sun, Z., To, S., Zhang, S., & Zhang, G. (2018). Theoretical and experimental investigation into non-uniformity of surface generation in micro-milling. International Journal of Mechanical Sciences, 140, 313-324.
[20]Li, Z., Sato, R., Shirase, K., Ihara, Y., & Milutinovic, D. S. (2019). Sensitivity analysis of relationship between error motions and machined shape errors in five-axis machining center - Peripheral milling using square-end mill as test case. Precision Engineering, 60, 28-41.
[21]Zhou, Y., Wang, S., Wang, L., Tang, J., & Chen, Z. C. (2019). CNC milling of face gears with a novel geometric analysis. Mechanism and Machine Theory, 139, 46-65.
[22]Biondani, F. G., & Bissacco, G. (2019). Effect of cutting edge micro geometry on surface generation in ball end milling. CIRP Annals, 68(1), 571-574.
[23]Dai, F., Geng, J., Ren, X., Huang, S., Zhou, J., Hua, X., & Chen, X. (2019). Surface roughness control of LY2 aluminum alloy milled surface subjected to laser shock wave planishing processing. Applied Surface Science, 486, 121-127.
[24]Tsai, D.-M., & Rivera Molina, D. E. (2019). Morphology-based defect detection in machined surfaces with circular tool-mark patterns. Measurement, 134, 209-217.
[25]Wang, J., Fu, P., & Gao, R. X. (2019). Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51, 52-60.
[26]Huynh, T.-C., Park, J.-H., Jung, H.-J., & Kim, J.-T. (2019). Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing. Automation in Construction, 105.
[27]Verl, A., Valente, A., Melkote, S., Brecher, C., Ozturk, E., & Tunc, L. T. (2019). Robots in machining. CIRP Annals, 68(2), 799-822.
[28]Borysenko, D., Karpuschewski, B., Welzel, F., Kundrák, J., & Felhő, C. (2019). Influence of cutting ratio and tool macro geometry on process characteristics and workpiece conditions in face milling. CIRP Journal of Manufacturing Science and Technology, 24, 1-5.
[29]Ardunio, https://www.arduino.cc/
[30]MEAN WELL, https://www.meanwell.com
[31]宸軒科技,https://www.chtco.com.tw/
[32]宇創視覺科技, http://www.viswell.com.tw/products/index3.php?id=130
[33]Basler,https://www.baslerweb.com
[34]Kowa,https://lenses.kowa-usa.com/
[35]Vital Vsion,htps://vitalvisiontechnology.com/
[36]丸榮, http://www.acrow-tools.com.tw/
[37]Halcon, https://www.mvtec.com/products/halcon
[38]端銑刀各部角度名稱, http://ntm.com.tw/technique.php#4
[39]Mitutoyo, https://www.mitutoyo.com.tw/product/category.php?id=12
[40]表面粗糙度值對照表, http://www.shenhay.com.tw/channel.asp?id=136
[41]刀具設定儀, http://www.keejaan.com/tp-3040r.html
[42]刀具影像量測儀,http://www.keejaan.com/kj-340a.html