Estimation of surface roughness for turning operations using image texture features

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 225, Issue 8, 2011, Pages 1281-1292

  Gadelmawla, E S ^a  

^a QASSIM UNIVERSITY   (Saudi Arabia)

Author keywords

Computer vision; Image processing; Surface roughness; Texture Features; Turning operation

Indexed keywords

AVERAGE HEIGHT; CORRELATION COEFFICIENT; GREY-LEVEL CO-OCCURRENCE MATRIXES; HIGHLY-CORRELATED; IMAGE TEXTURE; TEXTURE FEATURES; TURNING OPERATION; TURNING OPERATIONS; VISION SYSTEMS; WORK PIECES;

COMPUTER VISION; ESTIMATION; QUALITY CONTROL; SURFACE PROPERTIES; TEXTURES; TURNING;

SURFACE ROUGHNESS;

EID: 80054739865     PISSN: 09544054     EISSN: 20412975     Source Type: Journal    
DOI: 10.1177/2041297510393643     Document Type: Conference Paper

References (43)

1. Singh, S. K., Srinivasan, K., and Chakraborty, D. Acoustic characterization and prediction of surface roughness. J. Mater. Process. Technol., 2004, 152, 127-130.
2. Stout, K. J. and Blunt, L. Three-dimensional surface topography, 2, 2000, pp. 44-49 (Penton Press, an imprint of Kogan Page Ltd, London).
3. Chen, J. C. and Savage, M. A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Int. J. Adv. Mfg Technol., 2001, 17, 670-676. (Pubitemid 32914763)
4. Lu, R. S., Tian, G. Y., and Gledhill, D. Grinding surface roughness measurement based on the cooccurrence matrix of speckle pattern texture. Appl. Optics, 2006, 45(35), 1-9.
5. Elango, V. and Karunamoorthy, L. Effect of lighting conditions in the study of surface roughness by machine vision - an experimental design approach. Int. J. Adv. Mfg Technol., 2008, 37(1-2), 92-103. (Pubitemid 351651603)
6. Chen, Y. Q. Novel techniques for image texture classification. PhD thesis, 1995 (University of Southampton, UK).
7. Haralick, R. M., Shanmugam, K., and Dinstein, I. Textural features for image classification. IEEE Trans. on Systems Man and Cybernetics, 1973, 3, 610-621.
8. Lei, Wang and Jun, Liu Texture classification using multi-resolution Markov random field models. Pattern Recognition Lett., 1999, 20(2), 171-182.
9. PietikSainen, M., Ojala, T., and Xu, Z. Rotationinvariant texture classification using feature distributions. Pattern Recognition, 2000, 33, 43-52. (Pubitemid 32081877)
10. Jain, A. K. and Farrokhnia, F. Un-supervised texture segmentation using Gabor, lters. Pattern Recognition, 1991, 24(12), 1167-1186.
11. Randen, T. and HusHy, J. H. Multichannel filtering for image texture segmentation. Opt. Engng, 1994, 33(8), 2617-2625.
12. Jing-Wein, Wang, Chin-Hsing, Chen, Wei-Ming, Chien, and Chih-Ming, Tsai Texture classification using non-separable two-dimensional wavelets. Pattern Recognition Lett., 1998, 19(13), 1225-1234.
13. Abouelela, A., Abbas, H. M., Eldeeb, H., Wahdan, A. A., and Nassar, S. M. Automated vision system for localizing structural defects in textile fabrics. Pattern Recognition Lett., 2005, 26(10), 1435-1443. (Pubitemid 40744184)
14. Carfagni, M., Furferi, R., and Governi, L. A real-time machine-vision system for monitoring the textile raising process. Computers in Industry, 2005, 56(8-9), 831-842. (Pubitemid 41605986)
15. Chandraratne, M. R., Samarasinghe, S., Kulasiri, D., and Bickerstaffe, R. Prediction of lamb tenderness using image surface texture features. J. Food Engng, 2005, 28, 1-8.
16. Paliwal, J., Visen, N. S., Jayas, D. S., and White, N. D. G. Cereal grain and dockage identification using machine vision. Biosystems Engng, 2003, 85, 51-57. (Pubitemid 36579926)
17. Thybo, A. K., Szczypiński, P. M., Karlsson, A. H., Dønstrup, S., Stødkilde-Jørgensen, H. S., and Andersen, H. J. Prediction of sensory texture quality attributes of cooked potatoes byNMRimaging (MRI) of raw potatoes in combination with different image analysis methods. J. Food Engng, 2004, 61, 91-100.
18. Karkanis, S. A., Iakovidis, D. K., Maroulis, D. E., Karras, D. A., and Tzivras, M. Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. on Inf. Technol. Biomedicine, 2003, 7, 141-152.
19. Tahir, M. A., Bouridane, A., and Kurugollu, F. An FPGA based coprocessor for GLCM and Haralick texture features and their application in prostate cancer classification. Analog Integr. Circ. Signal Process, 2005, 43, 205-215.
20. Baraldi, A. and Parmiggiani, F. An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Trans. on Geosci. Remote Sensors, 1995, 33(2), 293-304.
21. Torabi, M., Ardekani, R. D., and Fatemizadeh, E. Discrimination between alzheimerapos disease and control group in MR-image based on texture analysis using artificial neural network11-14Int. Conf. on Biomed. and Pharmaceut. Engng, 2006, pp. 79-83.
22. Mayumi, D., Sabino, U., Costa, L. F., Rizatti, E. G., and Zago, M. A. A texture approach to leukocyte recognition. Special Issue on Imaging in Bioformatics Part III, Real-time imaging, 10, 2008, pp. 205-216 (Academic Press, New York).
23. Iivarinen, J., Heikkinen, K., Rauhamaa, J., Vuorimaa, P., and Visa, A. A defect detection scheme for web surface inspection. Int. J. Pattern Recognition Artif. Intell., 2000, 735-755.
24. Gadelmawla, E. S. A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT&E Int., 2004, 37, 577-588.
25. Iakovidis, D. K., Maroulis, D. D., and Bariamis, D. G. FPGA architecture for fast parallel computation of co-occurrence matrices. Microprocessors and Microsystems, 2007, 31, 160-165. (Pubitemid 46240300)
26. Siéler, L., Tanougast, C., and Bouridane, A. A scalable and embedded FPGA architecture for efficient computation of grey level co-occurrence matrices and Haralick textures features. Microprocessors and Microsystems, 2010, 34, 14-24.
27. Chen, Lu Study on prediction of surface quality in machining process. J. Mater. Process. Technol., 2008, 205, 439-450.
28. Benardos, P. G. and Vosniakos, G. C. Predicting surface roughness in machining: a review. Int. J. Mach. Tools Mf., 2003, 43, 833-844.
29. Zixin, Chen, Zhisheng, Zhang, Jinfei, Shi, Ruwen, Chen, Ren, Huang, and Chaofeng, Zhang A multivariate method for surface roughness vision inspection in different ambient light, Proceedings of the 2008 IEEE International Conference on Mechatronics and automation (ICMA 2008) 5-8 August. 2008, pp. 324-328.
30. Xin, N. D. and Wang, G. GLCM texture based fractal method for evaluating fabric surface roughness, Canadian Conference on Electrical and computer engineering (CCECE '09) 3-6 May. 2009, pp. 104-107 (John's, Newfoundland).
31. Chuen-Lin, Tien, You-Ru, Lyu, and Shiao-Shan, Jyu Surface flatness of optical thin films evaluated by gray level co-occurrence matrix and entropy. Appl. Surf. Sci., 2008, 254, 4762-4767.
32. Alvarenga, A. V., Teixeira, C. A., Ruano, M. G., and Pereira, W. C. A. Influence of temperature variations on the entropy and correlation of the grey-level co-occurrence matrix from B-mode images. Ultrasonics, 2010, 50, 290-293.
33. Lee, B. Y. and Tarng, Y. S. Surface roughness inspection by computer vision in turning operations. Int. J. Machine Tools Mf., 2001, 41, 1251-1263. (Pubitemid 32303082)
34. Lee, B. Y., Juan, H., and Yu, S. F. A study of computer vision for measuring surface roughness in the turning process. Int. J. Adv. Mfg Technol., 2002, 19, 295-301. (Pubitemid 34268711)
35. Lee, B. Y., Yu, S. F., and Juan, H. The model of surface roughness inspection by vision system in turning. Mechatronics, 2004, 14(1), 129-141.
36. Kuang-Chyi, Lee, Shinn-Jang, Ho, and Shinn-Ying, Ho Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precision Engng, 2005, 29, 95-100.
37. Al-Kindi, G. A. and Shirinzadeh, B. An evaluation of surface roughness parameters measurement using vision-based data. Int. J. Machine Tools Mf., 2007, 47, 697-708. (Pubitemid 44908877)
38. Gadelmawla, E. S., Eladawi, A. E., Abouelatta, O. B., and Elewa, I. M. Investigation of the cutting conditions in milling operations using image texture features. Proc. ImechE, Part B: J. Engineering Manufacture, 2008, 222(B11), 1395-1404.
39. MEIJI Techno Inc. http://www.meijitechno.com/ ml5000.htm.
40. Pixera corporation www.pixera.com.
41. Gadelmawla, E. S., Eladawi, A. E., Abouelatta, O. B., and Elewa, I. M. Application of computer vision for the prediction of cutting conditions in milling operations. Proc. ImechE, Part B: J. Engineering Manufacture, 2009, 223(B7), 791-800.
42. Gadelmawla, E. S., Koura, M. M., Maksoud, T. M. A., and Elewa, I. M., Soliman H. H. Roughness parameters. J. Mater. Process. Technol, 2002, 123(1), 133-145. (Pubitemid 34249719)
43. Handysurf E-10, Advanced Metrology Systems Limited, OADBY, Leicester, UK.