本研究成功開發自動對焦CNC(Computer Numerical Control)的概念機台，採用最符合成本的紅外線感測器，透過感測器實驗與分析去克服低成本感測器的缺點，並將球形曲面作為工件進行切削，測試機台的問題與缺點，根據研究結果將其改善。開發自動對焦CNC包含硬體與軟體兩部分，硬體設計以鋁擠型搭建一台龍門式CNC，並於機台上安裝第四軸(旋轉軸)、主軸雕刻刀、紅外線感測器，並以加強機台剛性為設計之首要目的；軟體部分使用Python撰寫了各式模組，使機台可以順利驅動馬達至指定目標點掃描目標，並將掃描值整合原始2D路徑檔，生成新的3D路徑檔作為機台最終切削檔。本實驗可發現，機台最小誤差範圍能控制在±1 mm 之內，加上機台使用上會有微小的失步，導致雕刻刀最小直徑只能至2 mm，選擇太細會出現斷刀情形；在切割字型與符號時，因路徑軌跡較單調，雕刻結果較為完美，當要雕刻複雜的照片時，因雕刻刀較粗，只能大致呈現圖形輪廓，線條較為複雜精細的部分會有失真現象。

This research successfully developed the conceptual machine of auto-focus CNC in using the most cost-effective infrared sensor. Through the sensor experiment and analysis, we overcome the shortcomings of the low-cost sensor.
The hardware materials base on aluminum extruded section, including 4-axis, sensor and spindle on it. Using Python to write a PWM generator, path-file conversion module, sensing module, and G-code interpretation module. Making the software combined with hardware, so that the machine can run smoothly. In this experiment, it can be found that the minimum error of the sensor can be controlled within ±1 mm, so we let the depth of the cutting system in 2 mm. In addition, when the machine is used for a while, there will be a slight losing step on it. So the minimum diameter of the engraving knife can only reach in 2 mm. If the engraving knife is too thin, it will be broken. If we cut the fonts or symbols, the result will be perfect. Because the trajectory is simple enough. If we cut the complex photos, the internal prototype will be distorted. But it also can see the contour on it.

[1] H. Liu, E. Miao, X. Zhuang and X. Wei. Thermal error robust modeling method for CNC machine tools based on a split unbiased estimation algorithm. Elsevier BV. 2017.
[2] K. E. Hendrickson. The Encyclopedia of the Industrial Revolution in World History. Rowman & Littlefield. 2010.
[3] E. Manchester. Transactions - Manchester Association of Engineers. Manchester Association of Engineers. 2020.
[4] 王玉倉。科學技術史。中國人民大學出版社。1993。
[5] K. Hanson. Machining for Dummies. Dummies. 2017.
[6] U.S.A. National Commission on Technology, “Technology and the American Economy,” pp. 274, 1966.
[7] 王松浩、王敬期和吳世雄。進階三軸銑削數控加工及實習。五南文化事業出版。2017。
[8] 廖文成，不同刀具路徑規劃銑削性能之分析與比較，國立虎尾科技大學機械與機電工程研究所碩士論文，2009。
[9] M. E. Martellotti, “An Analysis of the Milling Process,” Transaction of ASME, 63, pp. 677-700, 1941.
[10] J. Kloypayan and Y. S. Lee, “ Material engagement analysis of different endmills for adaptive feedrate control in milling processes, ” Computers in Industry, 47, pp. 55-76, 2002.
[11] H. S. Choy and K.W. Chan, “A corner-looping based tool path for pocket milling,” Computer-Aided Design, 35, pp. 155-166, 2003.
[12] C. C. Lo, “A new approach to CNC tool path generation,” Computer-Aided Design, 30, pp. 649-655, 1998.
[13] S. C. Park and B. K. Choi, “Tool-path planning for direction-parallel area milling, Computer Aided Design,” Computer-Aided Design, 32, pp. 17-25, 2000.
[14] A. Kaldos, I. F. Dagiloke and A. Boyle, “Computer aided cutting process parameter selection for high speed milling,” Journal of Materials Processing Technology, 61, pp. 219-224, 1996.
[15] J. G. Choi and M. Y. Yang, “In-process prediction of cutting depths in end milling,” International Journal of Machine Tools and Manufacture, 39, pp. 705-721, 1999.
[16] L. Zheng, Y. S. Chiou and S. Y. Liang, “Three Dimensional Cutting Force Analysis in End Milling,” International Journal of Mechanical Sciences, 38, pp. 259-269, 1996.
[17] 柳義耿，高速銑削之切削力研究，國立中興大學機械工程研究所碩士論文，1991。
[18] 李國田和莊華益，主軸系統切削力及顫振估測研究，電機月刊， 6，pp.135-141, 1996。
[19] 翁偉宏，高速銑削之定值切削力研究，國立海洋大學機械與輪機工程所碩士論文，2000。
[20] 徐文興，全高分子元件組成的紅外/近外光距離感測器，國立清華大學電子工程研究所碩士論文，2019。
[21] J. Borenstein and Y. Koren, “High-speed obstacle avoidance for mobile robots in Intelligent Control Proceedings,” IEEE International IC Symposium, pp. 382-384,1988.
[22] M. J. Seikavandi, “Low-cost 3D scanning using ultrasonic and camera data fusion for CNC Engraving Laser-Based Machine,” IEEE International MVIP Conference, pp. 1-3, 2020.
[23] C. Ya-Chun, et al., “Novel application of a laser range finder with vision system for wheeled mobile robot,” IEEE International AIM Conference, pp. 280-285, 2008.
[24] J. Songmin, et al., “Mobile robot localization and mapping based on mixed model,” IEEE International ICACTE Conference, 5, pp. 9-14, 2010.
[25] STMicroelectrioics, “VL6180 datasheet”, pp. 32, 2021.
[26] Broadcom Limited, “BCM2835 ARM Peripheral datasheet”, pp. 63, 2012.