This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Optical Surface Roughness Evaluation of Ground Specimens Using Speckle Line Images
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 25, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
A well-established method of surface roughness measurement is of stylus-based. The filtering effect of the stylus tip is the major lacuna of the process. So in the present study, a vision based 100% inspection procedure is proposed for the characterization of ground specimens. A CMOS camera, and monochromatic line laser source were used for capturing speckle line images of the ground specimens. Signal vectors were generated from each of the surface images of ground specimens using MATLAB software. On the other hand the roughness of the ground specimens, particularly the Arithmetic roughness average (Ra) & Arithmetic mean slope (Rda) were computed using a stylus instrument. It was found that standard deviation and kurtosis having good correlation with the image pixel intensity of the signal vectors with the correlation coefficient of 0.96 & 0.89 for Ra and 0.86 & 0.82 for Rda respectively. So we conclude that the speckle line images can be used for in-situ surface roughness evaluation of the ground specimens.
CitationJ, M., H PhD, S., Mariappan, M., and S, M., "Optical Surface Roughness Evaluation of Ground Specimens Using Speckle Line Images," SAE Technical Paper 2020-28-0514, 2020.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Alkoot, F.M. , “A Review on Advances in Iris Recognition Methods,” International Journal of Computer Engineering Research 3:1):1-1):9, 2012.
- Abhyankar, A., and Schuckers, S. , “A Novel Biorthogonal Wavelet Network System for Off-Angle Iris Recognition,” Pattern Recognition 43(3):987-1007, 2010.
- Daugman, J. , “How Iris Recognition Works,” in: The essential Guide to Image Processing (Elsevier, 2009), 715-739.
- Ali, M., Jailani, S., Mariappan, M., Anandan, M. et al. , “Vision Based Surface Roughness Characterization of Flat Surfaces Machined with EDM,” SAE Technical Paper 2019-28-0148, 2019, https://doi.org/10.4271/2019-28-0148.
- Fujii, H., and Asakura, T. , “Effect of Surface Roughness on the Statistical Distribution of Image Speckle Intensity,” Optics Communications 11(1):35-38, 1974.
- Fujii, H., and Asakura, T. , “Statistical Properties of Image Speckle Patterns in Partially Coherent Light,” Nouvelle Revue d’Optique 6(1):5, 1975.
- Fujii, H., and Asakura, T. , “Roughness Measurements of Metal Surfaces Using Laser Speckle,” JOSA 67(9):1171-1176, 1977.
- Fujii, H., Asakura, T., and Shindo, Y. , “Measurement of Surface Roughness Properties by Using Image Speckle Contrast,” JOSA 66(11):1217-1222, 1976.
- Gao, Z., and Zhao, X. , “Roughness Measurement of Moving Weak-Scattering Surface by Dynamic Speckle Image,” Optics and Lasers in Engineering 50(5):668-677, 2012.
- Rodríguez, F., Cotto, I., Dasilva, S., Rey, P. et al. , “Speckle Characterization of Surface Roughness Obtained by Laser Texturing,” Procedia Manufacturing 13:519-525, 2017.
- Sodhi, M.S., and Tiliouine, K. , “Surface Roughness Monitoring Using Computer Vision,” International Journal of Machine Tools and Manufacture 36(7):817-828, 1996.
- Ali, J.M., Jailani, H.S., and Murugan, M. , “Surface Roughness Evaluation of Milled Surfaces by Image Processing of Speckle and White-Light Images,” Advances in Manufacturing Processes 141-151, 2019.
- Suhail, S.M., Ali, J.M., Jailani, H.S., and Murugan, M. , “Vision Based System for Surface Roughness Characterisation of Milled Surfaces Using Speckle Line Images,” IOP Conference Series: Materials Science and Engineering 1:012054, 2018.
- Lu, R.-S., Tian, G.-Y., Gledhill, D., and Ward, S. , “Grinding Surface Roughness Measurement Based on the Co-occurrence Matrix of Speckle Pattern Texture,” Applied Optics 45(35):8839-8847, 2006.
- ALI, J., Jailani, H., and Murugan, M. , Surface Roughness Evaluation of Milled Steel Surfaces Using Wavelet Transform of Laser Speckle Line Images (Lasers in Engineering (Old City Publishing, 2019), 44.
- Ali, J.M., Jailani, H.S., and Murugan, M. , “Surface Roughness Evaluation of Electrical Discharge machiNed Surfaces Using Wavelet Transform of Speckle Line Images,” Measurement 149:107029, 2020.