Use of descriptors based on moments from digital images for tool wear monitoring

International Journal of Machine Tools and Manufacture

Volumn 48, Issue 9, 2008, Pages 1005-1013

  Barreiro, J ^a   Castejon M ^a   Alegre, E ^a   Hernandez L K ^b  

^a UNIVERSITY OF LEÓN   (Spain)

^b UNIVERSIDAD DE PAMPLONA   (Colombia)

Author keywords

Cluster analysis; Cutting tool; Image analysis; Wear

Indexed keywords

CLUSTER ANALYSIS; COST ACCOUNTING; DISCRIMINANT ANALYSIS; IMAGE ANALYSIS; WEAR OF MATERIALS;

FOWLKES-MALLOW INDEX; LEGENDRE DESCRIPTORS; LINEAR DISCRIMINANT ANALYSIS; TOOL WEAR MONITORING;

CUTTING TOOLS;

EID: 43049116797     PISSN: 08906955     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmachtools.2008.01.005     Document Type: Article

References (58)

1. Kurada S., and Bradley C. A review of machine vision sensors for tool condition monitoring. Computers in Industry 34 (1997) 55-72
2. Teti R. Machining of composite materials. Annals of the CIRP 51 2 (2002) 611-634
3. Weckenmann A., and Nalbantic K. Precision measurement of cutting tools with two matched optical 3d-sensors. Annals of CIRP 52 1 (2003) 443-446
4. Kopac J., and Sali S. Tool wear monitoring during the turning process. Journal of Materials Processing Technology 113 (2001) 312-316
5. ISO 3685:1993, Tool-life testing with single-point turning tools.
6. ISO 8688-1:1989, Tool life testing in milling-Part 1: face milling.
7. ISO 8688-2:1989, Tool life testing in milling-Part 2: end milling.
8. Lim G. Tool-wear monitoring in machine turning. Journal of Materials Processing Technology 51 (1995) 25-36
9. Al-Sulaiman F., Baseer M., and Sheikh A. Use of electrical power for online monitoring of tool condition. Journal of Materials Processing Technology 166 (2005) 364-371
10. Axinte D., Gindy N., Fox K., and Unanue I. Process monitoring to assist the workpiece surface quality in machining. International Journal of Machine Tools and Manufacture 44 (2004) 1091-1108
11. Cakir M., and Isik Y. Detecting tool breakage in turning AISI 1050 steel using coated and uncoated cutting tools. Journal of Materials Processing Technology 159 (2005) 191-198
12. Choudhury S., and Srinivas P. Tool wear prediction in turning. Journal of Materials Processing Technology 153-154 (2004) 276-280
13. Gajewski J., and Jonak J. Utilisation of neural networks to identify the status of the cutting tool point. Tunnelling and Underground Space Technology 21 (2006) 180-184
14. Ghosh N., Ravi Y., Patra A., Mukhopadhyay S., Paul S., Mohanty A., and Chattopadhyay A. Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing 21 1 (2007) 466-479
15. Haili W., Hua S., Ming C., and Dejin H. On-line breakage monitoring in turning. Journal of Materials Processing Technology 139 (2003) 237-242
16. Kuljanic E., and Sortino M. Twem, a method based on cutting forces-monitoring tool wear in face milling. International Journal of Machine Tools and Manufacture 45 (2005) 29-34
17. Ritou M., Garnier S., Furet B., and Hascoet J. A new versatile in-process monitoring system for milling. International Journal of Machine Tools and Manufacture 46 15 (2006) 2026-2035
18. Salgado D., and Alonso F. Tool wear detection in turning operations using singular spectrum analysis. Journal of Materials Processing Technology 171 (2006) 451-458
19. Sceffer C., and Heyns P. An industrial tool wear monitoring system for interrupted turning. Mechanical Systems and Signal Processing 18 (2004) 1219-1242
20. Shao H., Wang H., and Zhao X. A cutting power model for tool wear monitoring in milling. International Journal of Machine Tools and Manufacture 44 (2004) 1503-1509
21. Jurkovic J., Korosec M., and Kopac J. New approach in tool wear measuring technique using ccd vision system. International Journal of Machine Tools and Manufacture 45 9 (2005) 1023-1030
22. Kurada S., and Bradley C. A machine vision system for tool wear assessment. Tribology International 30 4 (1997) 295-304
23. Sick B. On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing 16 4 (2002) 487-546
24. Wang W., Wong Y., and Hong G. Flank wear measurement by successive image analysis. Computers in Industry 56 (2005) 816-830
25. Alegre E., Barreiro J., Cáceres H., Hernández L., Fernández R., and Castejón M. Design of a computer vision system to estimate tool wearing. Materials Science Forum 526 (2006) 61-66
26. Lanzetta M. A new flexible high-resolution vision sensor for tool condition monitoring. Journal of Materials Processing Technology 119 1-3 (2001) 73-82
27. Sortino M. Application of statistical filtering for optical detection of tool wear. International Journal of Machine Tools and Manufacture 43 (2003) 493-497
28. Wang W., Hong G., and Wong Y. Flank wear measurement by a threshold independent method with sub-pixel accuracy. International Journal of Machine Tools and Manufacture 46 2 (2006) 199-207
29. Obikawa T., and Shinozuka J. Monitoring of flank wear of coated tools in high speed machining with a neural network art 2. International Journal Machine Tools and Manufacture 44 (2004) 1311-1318
30. Brezak D., Udiljak T., Mihoci K., Majetic D., Novakovic B., and Kasac J. Tool wear monitoring using radial basis function neural network. Neural Networks 3 (2004) 25-29
31. Littlefair G., Javed M., and Smith G. Fusion of integrated multisensor data for tool wear monitoring. Neural Networks 2 (1995) 734-737
32. Sunil V., and Yung C. Design and implementation of tool wear monitoring with radial basis function neural networks. American Control Conference 3 (1995) 1722-1726
33. H. Guo, G. Xiong, F. Xi, J. Zheng, X. Ping, Detection and compensation of cutting tool wear based on Lipschitz exponent, in: Proceedings of the 2005 IEEE Conference on Control Applications, 2005.
34. L. Hernández, H. Cáceres, J. Barreiro, E. Alegre, M. Castejón, R. A. Fernández, Monitorización del desgaste de plaquitas de corte usando visión artificial, Congreso Iberoamericano de Ingeniería Mecánica (2005).
35. Teh C., and Chin R.T. On image analysis by the methods of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 10 4 (1988) 498-514
36. A. Choksuriwong, H. Laurent, B. Emil, Comparison of invariant descriptors for object recognition, in: IEEE International Conference on Image Processing, 2005, pp. 377-380.
37. W. Kim, P. Yuan, A practical pattern recognition system for translation, scale and rotation invariance, IEEE (1994) 391-396.
38. E. Alegre, L. Sánchez, R. Alaiz, J. C. Rodríguez, Utilización de momentos estadísticos y redes neuronales en la clasificación de cabezas de espermatozoides de verraco, XXV Jornada de Automatica (2004).
39. J.S. Park, D. Chang, 2D invariant descriptors for shape-based images retrieval, Pattern Recognition Letters (2001).
40. Prokop R.J., and Reeves A.P. A survey of moment-based techniques for unoccluded object representation and recognition. CCVGIP: Graphical Models and Image Processing 54 5 (1992) 438-460
41. Hu M. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory (1962) 179-182
42. Khotanzad A., and Hong Y.H. Invariant image recognition by Zernike moments. IEEE Transaction on Pattern Analysis and Machine Intelligence 22 1 (1990) 489-497
43. Taubin G., and Cooper D. Object recognition based on moment (or algebraic) invariants. Geometric Invariance in Computer Vision (1992) 375-397
44. J. Flusser, T. Suk, On the calculation of image moments, Research Report no. 1946.
45. Flusser J., Zitovi B., and Suk T. Invariant-based registration of rotated and blurred images. IEEE (1999) 1262-1265
46. Menéndez C., Ordieres J.B., and Ortega F. Importance of information pre-processing in the improvement of neural network results. Expert Systems 13 2 (1996) 95-102
47. Gonzalez Nicieza C., Ordieres Mere J.B., Menendez Diaz A., and Taboada Castro J. Optimizing of stripping in a slate bank. International Journal of Surface Mining and Reclamation 8 1 (1994) 17-22
48. Bello García A., Menéndez Díaz A., Ordieres Meré J.B., and González Nicieza C. Generalization of the influence function method in mining subsidence. International Journal of Surface Mining, Reclamation and Environment 10 4 (1996) 195-202
49. Pernía-Espinoza A.V., Ordieres-Meré J.B., Martínez-de-Pisón F.J., and González-Marcos A. TAO-robust backpropagation learning algorithm. Neural Networks 18 2 (2005) 191-204
50. Meré J.B.O., Marcos A.G., González J.A., and Rubio V.L. Estimation of mechanical properties of steel strip in hot dip galvanising lines. Ironmaking and Steelmaking 31 1 (2004) 43-50
51. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer, Berlin
52. Banfield J., and Raftery A. Model-based Gaussian and non-Gaussian Clustering. Biometrics 49 (1993) 803-821
53. Fraley C., and Raftery A.E. Enhanced software for model-based clustering. discriminant analysis and density estimation: Mclust, Journal of Classification 20 (2003) 263-286
54. McLachlan G., and Krishnan T. The EM Algorithm and Extensions (1997), Wiley, New York
55. McLachlan G., and Peel D. Finite Mixture Models (2000), Wiley, New York
56. McLachlan G. Dis criminant Analysis and Statistical Pattern Recognition (2004), Wiley, New York
57. M. Meila, Comparing clusterings, Technical Report, University of Washington (2002).
58. Castejón M., Alegre E., Barreiro J., and Hernández L. On-line tool wear monitoring using geometric descriptors from digital images. International Journal of Machine Tools and Manufacture 47 (2007) 1847-1853