Optical Image Inspection of Cutting Tool Geometry for Grinding Machines


Article Preview

The purpose of this paper is to develop a tool image inspection and measuring system by C++ Builder. Firstly, tool images are captured online for geometry analysis via a disassembled inspection mechanism mounted on the Z-axis of a five-axis tool grinding machine. One can use the controller of the machine to set the coordinate location of the mechanism and implement the humanized functions of autofocusing and automatic measurements. The digital images are calculated by the subpixel approach to improve the measurement resolution, and filtered the edge point location by Hough transform to upgrade the precision. The human machine interface (HMI) has a tutoring manner for users to operate the measuring procedures. These proposed functions can measure the geometric dimension such as the diameter, radius, and angle of different end mills or drills after finishing the tool grinding processes. Furthermore, the grinding processes can refer the online measured results to compensate the tool dimension. Therefore, this online image inspection and measuring system can improve the precision of tool grinding, product quality, and reduce the product cost. Finally, experiments are presented to show that the repeatability errors are ± 2 μm and ± 1 μm for the diameter and the radius measurements of end mills, respectively. The percentage error is 0.116% for measuring the point angle of a drill. Thus, the results demonstrate the effectiveness of the proposed method that can be employed to measure tool geometry of different cutting tools.



Edited by:

Zone-Ching Lin, You-Min Huang, Chao-Chang Arthur Chen and Liang-Kuang Chen




J. Y. Chen et al., "Optical Image Inspection of Cutting Tool Geometry for Grinding Machines", Advanced Materials Research, Vol. 579, pp. 235-242, 2012

Online since:

October 2012




[1] H. Schulz, S. Hock, High-speed milling of die and molds-cutting conditions and technology, Annals of the CIRP, 44 (1995) 35-38.

DOI: https://doi.org/10.1016/s0007-8506(07)62270-7

[2] R.C. Dewes, D.K. Aspinwall, A review of ultra high-speed milling of hardened steels, Journal of Materials Processing Technology, 69 (1997) 1-17.

DOI: https://doi.org/10.1016/s0924-0136(96)00042-8

[3] S. Malkin, C. Guo, Grinding Technology: Theory and Applications of Machining with Abrasives, Industrial Press, New York, (2008).

[4] United State Cutting Tool Institute, Metal Cutting Tool Handbook, Industrial Press, New York, (1989).

[5] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Pearson Education, New Jersey, (2008).

[6] J.U. Jeon, S.W. Kim, Optical flank wear monitoring of cutting tools by image processing, Wear, (1988) 207-217.

DOI: https://doi.org/10.1016/0043-1648(88)90131-7

[7] J. Jurkovic, M. Korosec and J. Kopac, New approach in tool wear measuring technique using CCD vision system, International Journal of Machine Tools and Manufacture, 45 (2005) 1023-1030.

DOI: https://doi.org/10.1016/j.ijmachtools.2004.11.030

[8] W.H. Wang, G.S. Hong and Y.S. Wong, Flank wear measurement by a threshold independent method with sub-pixel accuracy, International Journal of Machine Tools and Manufacture, 46 (2006) 199-207.

DOI: https://doi.org/10.1016/j.ijmachtools.2005.04.006

[9] M. Castejon, E. Alegre, J. Barreiro and L.K. Hernandez, On-line tool wear monitoring using geometric descriptors from digital images, International Journal of Machine Tools and Manufacture, 47 (2007) 1847-1853.

DOI: https://doi.org/10.1016/j.ijmachtools.2007.04.001

[10] L. Hazra, H. Kato, T. Kiryu, Y. Hashimoto, T. Kuroda, Y. Tsuchiya and I. Sakuma, Inspection of reground drill point geometry using three silhouette images, Journal of Materials Processing Technology, 127 (2002) 169–173.

DOI: https://doi.org/10.1016/s0924-0136(02)00120-6

[11] M.A. Mannan, A.F. Kassim and M. Jing, Application of image and sound analysis techniques to monitor the condition of cutting tools, Pattern Recognition Letters, 21 (2000) 969-979.

DOI: https://doi.org/10.1016/s0167-8655(00)00050-7

[12] A.J. Vallejo, R. Morales-Menendez and J.R. Alique, On line cutting tool condition monitoring in machining processes using artificial intelligence, in: P. Pecherkova, M. Flidr and J. Dunik (Eds. ), Robotics, Automation and Control, InTech Publisher, Vienna, 2008, pp.143-166.

DOI: https://doi.org/10.5772/5833

[13] J.H. Kim, D.K. Moon, D.W. Lee, J.S. Kim, M.C. Kang and K.H. Kim, Tool wear measuring technique on the machine using CCD and exclusive jig, Journal of Materials Processing Technology, 130-131 (2002) 668-674.

DOI: https://doi.org/10.1016/s0924-0136(02)00733-1

[14] L. Hazra, H. Kato, T. Kuroda, Y. Hashimoto, Y. Tsuchiya and I. Sakuma, Practical inspection system of drill point geometry by using simple measurement jig and image processing, Precision Engineering, 25 (2001) 206-211.

DOI: https://doi.org/10.1016/s0141-6359(01)00071-x

[15] J.C. Su, C.K. Huang and Y.S. Tarng, An automated flank wear measurement of microdrills using machine vision, Journal of Materials Processing Technology, 180 (2006) 328–335.

DOI: https://doi.org/10.1016/j.jmatprotec.2006.07.001

[16] P. Fu, A.D. Hope, A hybrid pattern recognition architecture for cutting tool condition monitoring, in: P. -Y. Yin (Ed. ), Pattern Recognition Techniques, Technology and Applications, InTech Publisher, Vienna, 2008, pp.547-558.

DOI: https://doi.org/10.5772/6258

[17] W. Gao, T. Asai and Y. Arai, Precision and fast measurement of 3D cutting edge profiles of single point diamond micro-tools, Annals of the CIRP, 58 (2009) 451-454.

DOI: https://doi.org/10.1016/j.cirp.2009.03.009

[18] Z. Li, W. Zgang and D. Xiong, A practical method to determine rake angles of twist drill by measuring the cutting edge, International Journal of Machine Tools and Manufacture, 50 (2010) 747-751.

DOI: https://doi.org/10.1016/j.ijmachtools.2010.04.001

[19] Z.J. Qiu, F.Z. Fang, L.Y. Ding and Q.Z. Zhao, Investigation of diamond cutting tool lapping system based on machine image measurement, International Journal of Advanced Manufacturing Technology, 56 (2011) 79-86.

DOI: https://doi.org/10.1007/s00170-011-3168-y

[20] P.V.C. Hough, Methods and means for recognizing complex patterns, U.S. Patent 3, 069, 654. (1962).

[21] T.C. Chen, K.L. Chung, An efficient randomized algorithm for detecting circles, Computer Vision and Image Understanding, 83 (2001) 172-191.

DOI: https://doi.org/10.1006/cviu.2001.0923