Flank wear measurement by successive image analysis

In this paper, a system based on successive image analysis is proposed for periodic measurement of flank wear in milling. The successive images are captured while the spindle is rotating. The blur of the moving images is minimized by the use of a high-speed camera and low spindle speed during the image capture. A method based on image series to measure flank wear has been developed and successfully applied on these moving images. Its performance is compared with the method based on individual still or static images. The results show improved robustness of this system with high potential for industrial application to measure the flank wear in-cycle (between passes) without stopping the spindle.

[1]  Y. Maeda,et al.  Estimation of wear land width of cutting tool flank with the aid of digital image processing technique , 1987 .

[2]  Kazuaki Iwata,et al.  Estimation of Cutting Tool Life by Processing Tool Image Data with Neural Network , 1993 .

[3]  Elisabetta Ceretti,et al.  A Neural Network Architecture for Tool Wear Detection through Digital Camera Observations , 1996 .

[4]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[5]  Ju-Hyun Jeon,et al.  Optical flank wear monitoring of cutting tools by image processing , 1988 .

[6]  Min-Yang Yang,et al.  Crater wear measurement using computer vision and automatic focusing , 1996 .

[7]  Reinhard Klette,et al.  Handbook of image processing operators , 1996 .

[8]  Colin Bradley,et al.  A review of machine vision sensors for tool condition monitoring , 1997 .

[9]  A. Galip Ulsoy,et al.  On-Line Flank Wear Estimation Using an Adaptive Observer and Computer Vision, Part 1: Theory , 1993 .

[10]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[11]  K. N. Prasad,et al.  Tool wear evaluation by stereo vision and prediction by artificial neural network , 2001 .

[12]  A. Yamamoto,et al.  Contour mapping of cutting tool face with the aid of digital image processing technique , 1987 .

[13]  Wenhui Wang,et al.  Flank wear measurement by a threshold independent method with sub-pixel accuracy , 2006 .

[14]  Santanu Das,et al.  3D tool wear measurement and visualisation using stereo imaging , 1997 .

[15]  A. Galip Ulsoy,et al.  On-Line Flank Wear Estimation Using an Adaptive Observer and Computer Vision, Part 2: Experiment , 1993 .

[16]  Colin Bradley,et al.  A machine vision system for tool wear assessment , 1997 .

[17]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[18]  Tilo Pfeifer,et al.  Reliable tool wear monitoring by optimized image and illumination control in machine vision , 2000 .

[19]  S. M. Taboun,et al.  A machine vision system for wear monitoring and breakage detection of single-point cutting tools , 1994 .

[20]  F Giusti,et al.  On-Line Sensing of Flank and Crater Wear of Cutting Tools , 1987 .

[21]  Michele Lanzetta,et al.  A new flexible high-resolution vision sensor for tool condition monitoring , 2001 .

[22]  Young Shik Moon,et al.  An efficient method of estimating edge locations with subpixel accuracy in noisy images , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[23]  Kjeld Bruno Pedersen,et al.  Wear measurement of cutting tools by computer vision , 1990 .