To prevent the tool installation errors caused by frequent changes of tools during the aeronautical components machining, a detection system based on machine vision is proposed to measure geometric and state parameters of milling cutter. The end mill is taken as the research object, and its dynamic image contour under the state of spindle rotation is extracted. Besides, the measurement algorithm for cutting edge radius, tool diameter and overhang length are also developed. To demonstrate the visual deviation caused by the camera's large view during the measurement of overhang length, the deviation correction algorithm is further studied to improve the measurement accuracy. Finally, the proposed method is verified on the CNC machine tool. The results show that the maximum error is 0.51% and the device achieves good repeatability and high precision, which can realize the on-machine detection of tool geometric and state parameters.
[1] ZHANG C, ZHANG J L. On-line tool wear measurement for ball-end milling cutter based on ma-chine vision[J]. Computers in Industry, 2013, 64(6):708-719.
[2] DAI Y Q, ZHU K P. A machine vision system for micro-milling tool condition monitoring[J]. Precision Engineering, 2018, 52:183-191.
[3] ABUBAKR M, HASSAN M A, KROLCZYK G M, et al. Sensors selection for tool failure detection during machining processes:A simple accurate classification model[J]. CIRP Journal of Manufacturing Science and Technology, 2021, 32:108-119.
[4] 万鹏,李迎光,刘长青,等.基于域对抗门控网络的变工况刀具磨损精确预测方法[J].航空学报,2021,42(10):524879. WAN P, LI Y G, LIU C Q, et al. An accurate tool wear prediction method under varying cutting conditions based on domain adversarial gating neural network[JL]. Acta Aeronautica et Astronautica Sinica, 2021, 42(10):524879(in Chinese).
[5] ZHU A B, HE D Y, ZHAO J W, et al. Online tool wear condition monitoring using binocular vision[J]. Insight, 2017, 59(4):203-210.
[6] LI Y G, LIU C Q, HUA J Q, et al. A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning[J]. CIRP Annals-Manufacturing Technology, 2019, 68(1):487-490.
[7] NOURI M, FUSSELL B K, ZINITI B L, et al. Real-time tool wear monitoring in milling using a cutting condition independent method[J]. International Journal of Machine Tools&Manufacture, 2015, 89:1-13.
[8] LI W, LIU T. Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling[J]. Mechanical Systems and Signal Processing, 2019, 131:689-702.
[9] 单忠德,张飞,任永新,等.基于机器视觉铸件布氏硬度在线检测技术研究[J].机械工程学报, 2017, 53(1):157-164. SHAN Z D, ZHANG F, REN Y X, et al. On line detection technology of the hardness of cast iron parts based on machine vision[J]. Journal of Mechanical Engineering, 2017, 53(1):157-164(in Chinese).
[10] KASSIM A A, MANNAN M A, MIAN Z. Texture analysis methods for tool condition monitoring[J]. Image and Vision Computing, 2007, 25(7):1080-1090.
[11] JING M. Measurement method of screw thread geometric error based on machine vision[J]. Measurement and Control, 2018, 51(7-8):304-310.
[12] YUAN L, GUO T, QIU Z J, et al. Measurement of geometrical parameters of cutting tool based on focus variation technology[J]. International Journal of Advanced Manufacturing Technology, 2019, 105(5-6):2383-2391.
[13] 刘今越,刘佳斌,贾晓辉,等.基于面结构光投影法的刀具几何参数测量研究[J].仪器仪表学报, 2017, 38(5):1276-1284. LIU J Y, LIU J B, JIA X H, et al. Research on tool geometry parameter measurement based on surface structured light projection[J]. Chinese Journal of Instrumentation, 2017, 38(5):1276-1284(in Chinese).
[14] 尚波,张曦,司春迎,等.基于机器视觉的刀具状态在机检测方法的研究[J].计量与测试技术, 2017, 44(12):47-49. SHANG B, ZHANG X, SI C Y, et al. Research of on machine tool condition measurement system based on machine vision[J]. Metrology&Measurement Technique, 2017, 44(12):47-49(in Chinese).
[15] 侯秋林,孙杰,皇攀凌,等.基于机器视觉刀具几何参数检测算法与误差分析[J].山东大学学报(工学版), 2017, 47(4):77-82. HOU Q L, SUN J, HAUNG P L, et al. Algorithm and error analysis of tool geometric parameters detection based on machine vision[J]. Journal of Shandong University (Engineering Science), 2017, 47(4):77-82(in Chinese).
[16] BHARDWAJ D, PANKAJAKSHAN V. Image overlay text detection based on JPEG truncation error analysis[J]. IEEE Signal Processing Letters, 2016, 23(8):1027-1031.
[17] GOTFRYD M. Gauss filter-properties, realisation, application[J]. Elektronika, 2010, 51(4):88-92.
[18] MEYLAN L, SUSSTRUNK S. High dynamic range im-age rendering with a Retinex-based adaptive filter[J]. IEEE Transactions on Image Processing, 2006, 15(9):2820-2830.
[19] DONG H X, PRASAD D K, CHEN I M. Accurate detection of ellipses with false detection control at video rates using a gradient analysis[J]. Pattern Recognition, 2018, 81:112-130.
[20] 王聪,王海鹏,熊伟,等.一种基于最小二乘拟合的数据关联算法[J].航空学报, 2016, 37(5):1603-1613. WANG C, WANG H P, XIONG W, et al. Data sociation algorithm based on least square fitting[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(5):1603-1613(in Chinese).
[21] HU Z Y, WU F C. A review on some active vision based camera calibration techniques[J]. Chinese Journal of Computers, 2002, 25(11):1149-1156.
[22] RAMALINGAM S, STURM P. A unifying model for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(7):1309-1319.