The computer-based bootstrap method as a tool to select a relevant surface roughness parameter
Source abstract
Abstract
The aim of this paper is to present how to make the most of the recent and powerful statistical computer-based bootstrap method (CBBM) in roughness studies. This work shows that this statistical method can help to determine quantitatively, and without preconception, the most relevant roughness parameter that characterises the surface morphology of a manufactured product as far as a correlation with a particular function, property or application is concerned. The efficiency of this statistical method is illustrated in this paper describing the relationships between the brightness level and the surface roughness of cold-rolled low carbon steel strips; the relevance of 100 or so roughness parameters was studied via a computer software we have been upgrading for a few years.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Surface Roughness and Optical Measurements · Textile materials and evaluations · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (18)
Show source-provided references
1. T.R. Thomas, Rough Surfaces 2nd ed., Imperial College Press, London, 1999.
2. D.J. Whitehouse, Handbook of Surface Metrology, Institute of Physics, Bristol, 1994.
3. J.C. Russ, Fractal Surfaces, Plenum Press, New York, 1994.10.1007/978-1-4899-2578-7 ↗
4. Measurement of surface roughness on rough machined surfaces using spectral speckle correlation and image analysis10.1016/0043-1648(93)90424-K ↗
5. Characterization of surface roughness by laser scattering: diffusely scattered intensity measurement10.1016/S0043-1648(98)00333-0 ↗
6. Parameter rash—is there a cure?10.1016/0043-1648(82)90341-6 ↗
7. Bootstrap methods: another look at the jacknife10.1214/aos/1176344552 ↗
8. Bootstrap measures for standard errors, confidence intervals, and other measures of statistical accuracy10.1214/ss/1177013815 ↗
9. Statistical data analysis in the computer age10.1126/science.253.5018.390 ↗
10. B. Efron, R. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall, New York, 1993.10.1007/978-1-4899-4541-9 ↗
11. M. Bigerelle, Caractérisation géométrique des surfaces et interfaces: applications en métallurgie, Ph.D. Thesis, Ecole Nationale Supérieure d’Arts et Métiers, Lille, France, 1999.
12. P. Beckmann, A. Spizzichino, The Scattering of Electromagnetic Waves from Rough Surfaces, Pergamon Press, London, 1963.
13. E. Jackeman, Non-gaussian statistical models for scattering calculations, in: J.C. Dainty, D. Maystre (Eds.), Waves in Random Media, vol. 1, An Institute of Physics Journal, 1991, No. 3, pp. S109–S119.10.1088/0959-7174/1/3/009 ↗
14. R. Botet, E.Y. Poliakov, V.M. Shalaev, V.A. Markel, Fractal-surface-enhanced optical responses, in: J.L. Vehel, E. Lutton, C. Tricot (Eds.), Fractals in Engineering, Springer, London, 1997, pp. 237–251.10.1007/978-1-4471-0995-2_18 ↗
15. Regimes of surface roughness measurable with light scattering
16. M. Bigerelle, A. Iost, Calcul de la dimension fractale d’un profil par la méthode des autocorrélations moyennées normées (AMN), C.R. Acad. Sci., Série IIb 323 (1996) 669–675.
17. A new method to calculate fractal dimension of a surface: Application to the human cell proliferation10.1016/S0898-1221(01)00148-1 ↗
18. R.L. Packett, Principles of Regression Analysis, Oxford University Press, 1960.