Abstract
Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amiri, M., Rabiee, H.: A novel rotation/scale invariant template matching algorithm using weighted adaptive lifting scheme transform. Pattern Recognition 43(7), 2485–2496 (2010)
Atherton, T., Kerbyson, D.: Size invariant circle detection. Image and Vision Computing 17(11), 795–803 (1999)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008). Similarity Matching in Computer Vision and Multimedia
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI) 8(6), 679–698 (1986)
Castejón, M., Alegre, E., Barreiro, J., Hernández, L.: On-line tool wear monitoring using geometric descriptors from digital images. International Journal of Machine Tools and Manufacture 47(12–13), 1847–1853 (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005
Hough, P.: Method and Means for Recognizing Complex Patterns. U.S. Patent 3.069.654, December 1962
Jacobson, N., Nguyen, T., Crosby, R.: Curvature scale space application to distorted object recognition and classification. In: Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, ACSSC 2007, pp. 2110–2114, November 2007)
Jurkovic, J., Korosec, M., Kopac, J.: New approach in tool wear measuring technique using ccd vision system. International Journal of Machine Tools and Manufacture 45(9), 1023–1030 (2005)
Kim, J.H., Moon, D.K., Lee, D.W., Kim, J.S., Kang, M.C., Kim, K.H.: Tool wear measuring technique on the machine using ccd and exclusive jig. Journal of Materials Processing Technology 130–131, 668–674 (2002)
Lim, T., Ratnam, M.: Edge detection and measurement of nose radii of cutting tool inserts from scanned 2-d images. Optics and Lasers in Engineering 50(11), 1628–1642 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mukhopadhyay, P., Chaudhuri, B.B.: A survey of hough transform. Pattern Recognition 48(3), 993–1010 (2015)
Pfeifer, T., Wiegers, L.: Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28(3), 209–218 (2000)
Sortino, M.: Application of statistical filtering for optical detection of tool wear. International Journal of Machine Tools and Manufacture 43(5), 493–497 (2003)
Su, J., Huang, C., Tarng, Y.: An automated flank wear measurement of microdrills using machine vision. Journal of Materials Processing Technology 180(1–3), 328–335 (2006)
Sun, J., Sun, Q., Surgenor, B.: An adaptable automated visual inspection scheme through online learning. The International Journal of Advanced Manufacturing Technology 59(5–8), 655–667 (2012)
Teti, R.: Machining of composite materials. CIRP Annals - Manufacturing Technology 51(2), 611–634 (2002)
Wang, W., Hong, G., Wong, Y.: Flank wear measurement by a threshold independent method with sub-pixel accuracy. International Journal of Machine Tools and Manufacture 46(2), 199–207 (2006)
Weis, W.: Tool wear measurement on basis of optical sensors, vision systems and neuronal networks (application milling). In: Conference Record WESCON 1993, pp. 134–138, September 1993
Xiong, G., Liu, J., Avila, A.: Cutting tool wear measurement by using active contour model based image processing. In: 2011 International Conference on Mechatronics and Automation (ICMA), pp. 670–675, Auguest 2011
Yuen, H.K., Princen, J., Illingworth, J., Kittler, J.: A comparative study of hough transform methods for circle finding. In: Proc. 5th Alvey Vision Conf., Reading, pp. 169–174, Auguest 1989
Zhang, C., Zhang, J.: On-line tool wear measurement for ball-end milling cutter based on machine vision. Computers in Industry 64(6), 708–719 (2013)
Zuiderveld, K.: Graphics gems chap. iv. Contrast Limited Adaptive Histogram Equalization, pp. 474–485. Academic Press Professional Inc, San Diego, CA, USA (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N. (2015). Cutting Edge Localisation in an Edge Profile Milling Head. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-23117-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23116-7
Online ISBN: 978-3-319-23117-4
eBook Packages: Computer ScienceComputer Science (R0)