Intelligent Vision Methodology for Detection of the Cutting Tool Breakage

Full Text (PDF, 866KB), PP.41-49

Views: 0 Downloads: 0


Abdallah A. Alshennawy 1,* Ayman A. Aly 2,3

1. Design and prod. Eng. Dept. Tanta University, Egypt

2. Mechatronics Sec. Dept. of Mechanical Engineering, Faculty of Engineering, Assiut University, 71516, Assiut, Egypt

3. Mechatronics Sec. Dept. of Mechanical Engineering, Faculty of Engineering, Taif University, 888, Taif, Saudi Arabia

* Corresponding author.


Received: 3 Sep. 2012 / Revised: 20 Jan. 2013 / Accepted: 12 Mar. 2013 / Published: 8 May 2013

Index Terms

Intelligent Vision, Parts Classification, Chain Code, Diagnostics, Fuzzy Logic, Neural Networks


In this paper, a new Intelligent system based on neurofuzzy for detecting and diagnostics the wear and damage of the milling cutter is presented. The compatibility between the computer vision and neurofuzzy techniques is introduced. The proposed approaches consists of capturing the milling cutter image, Fuzzy edge detection, Chain code technique for feature extraction and finally, apply the neural network on the feature. The results of the study are three different diagnostics models, The first is diagnostic model for the original profile of the perfect cutter, the second is model for the wearied profile and the third is model for the damage profile. Experimental test results show that the proposed system is reliable, practical and can be used for the easy distinguish between the wear and damage automatically.

Cite This Paper

Abdallah A. Alshennawy, Ayman A. Aly, "Intelligent Vision Methodology for Detection of the Cutting Tool Breakage", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.6, pp.41-49, 2013. DOI:10.5815/ijitcs.2013.06.06


[1]Thomas R., "Computer Vision in Industry" in Artificial Intelligence in Engineering, John Wiley & Sons ltd 1991.

[2]Elias N. Malamasa, Euripides G.M. Petrakisa, Michalis Zervakisa, Laurent Petitb, " A survey on industrial vision systems, applications and tools "Department of Electronic and Computer Engineering, Technical University of Crete, Chania, Crete 73100, Belgium, Image and Vision Computing 21 (2003) 171–188

[3]B. J. Lei, Emile A. Hendriks, M.J.T. Reinders, On Feature Extraction from Images, MCCWS project, Information and Communication Theory Group TUDelft, Tuesday, June 01, 1999

[4]M.petrou, Learning in pattern recognition: some thoughts, Pattern Recognition Letters 22,3-13, 2001

[5]Verma B. and John Zakos, “A Computer-Aided Diagnosis System For Digital Mammograms Based On Fuzzy- Neural And Feature Extraction Techniques”, IEEE Transactions on Information Technology in Biomedicine, , volume. 5, March 2001.

[6]Yingxue Yao*, Xiaoli Li, Zhejun Yuan," Tool wear detection with fuzzy classification and wavelet fuzzy neural network " Department of Mechanical Engineering, Harbin Institute of Technology, Harbin 150001, People’s Republic of China Received 31 March 1998; received in revised form 22 January 1999

[7]Abdallah A. Alshnnaway, Ayman A. Aly, ”Fuzzy Logic Technique Applied to Extract Edge Detection in Digital Images For Two Dimensional Objects”, International conference in Production Engineering, METIP 4, 15-17 December 2006.

[8]Ayman A. Aly and A. A. Alshnnaway, "An Edge Detection and Filtering Mechanism of Two Dimensional Digital Objects Based on Fuzzy Inference", International Conference in Mechanical Engineering, ICME, pp. 247-251, Tokyo, Japan, May 27-29, 2009.

[9]B.-G. Hu, R. G. Gosine, L. X. Cao, and C. W. de Silva, ” Application of a Fuzzy Classification Technique in Computer Grading of Fish Products ”, IEEE Transactions on Fuzzy Systems, Vol. 6, No. 1, February 1998.

[10]S. Singh and A. Amin. Fuzzy Recognition of Chinese Characters, Proc. Irish Machine Vision and Image Processing Conference (IMVIP'99), Dublin, (8-9 September, 1999).

[11]Ayman A. Aly, H. Ohuchi and A. Abo-Ismail, “Fuzzy Model Reference Learning Control of 6-Axis Motion Base Manipulator”, 7th IEEE International Conference on Intelligent Engineering Systems, Luxer, March, 2003.

[12]Venu Govindaraju, Zhixin Shi CEDAR, Feature Extraction Using a Chain coded Contour Representation of Fingerprint Images, Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY 14260, John Schneider, Ultra-Scan Corporation, 4240 Ridge Lea Rd, Amherst, New York 14226 March 24, 2003

[13]Bryan S. Morse, Brigham Young University, Lecture 7: Shape Description (Contours),1998–2000, Last modified on January 21, 2000 at 2:20 PM

[14]Ahmet Denker and Tuˇgrul Adıg¨uzel, Vision Based Robotic Interception in Industrial Manipulation Tasks, international journal of computational intelligence volume 3 number 4 2006 issn 1304-2386

[15]S. Hoque K. Sirlantzis M. C. Fairhurst, A New Chain-code Quantization Approach Enabling High Performance Handwriting Recognition based on Multi-Classifier Schemes, Department of Electronics, University of Kent, Canterbury, Kent, United Kingdom, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) 0-7695-1960-1/03 $17.00 © 2003 IEEE

[16]chung-hsien huang, jiann-der lee, jau-hua huang, Registration of ct image and facial surface data using adaptive genetic algorithm, Biomed Eng Appl Basis Comm, 2005(August); 17: 201-206.

[17]Laurence Fauselt, Fundamentals of neural networks., prentice-Hall, 1994.

[18]Haykin, S., Neural networks; A comprehensive foundation, Prentice-Hall,New-Jersey, USA, 1994.

[19]C. Bahlmann, G. Heidemann, H. Ritter, Artificial neural networks for automated quality control of textile seams, Pattern Recognition 32, (1999) 1049–1060.

[20]A.R. Novini, Fundamentals of machine vision inspection in metal container glass manufacturing, Vision’90 Conference (1990).

[21]Neural-Fuzzy Feature Detectors Harvey A. Cohen Craig McKinnon , and J. You, "Neural-Fuzzy Feature Detectors" " DICTA-97, Auckland, N.Z., Dec 10-12, pp 479-484.

[22]Editorial: Neural-Fuzzy Applications in Computer Vision Journal of Intelligent and Robotic Systems 29: 309–315, 2000. © 2000 Kluwer Academic Publishers. Printed in the Netherlands.