Advanced Techniques for Assessment Surface Topography. by Liam Blunt and Xiangqian Jiang (Eds.)

By Liam Blunt and Xiangqian Jiang (Eds.)

Content material:

, Page vii
1 - advent: The heritage and present kingdom of 3D floor Characterisation

, Pages 1-13, Liam Blunt
2 - Numerical Parameters for Characterisation of Topography

, Pages 17-41, Liam Blunt, Xiangqian Jiang
3 - Novel Areal Characterisation Techniques

, Pages 43-61, Paul J. Scott
4 - complex Gaussian Filters

, Pages 63-90, Stefan Brinkman, Horst Bodschwinna
5 - Multi-scalar Filtration Methodologies

, Pages 91-115, Xiangqian Jiang
6 - Calibration systems for Stylus and Optical Instrumentation

, Pages 119-173, Jean François Ville
7 - Calibration strategies for Atomic strength Microscopes

, Pages 175-194, Anders Kühle
8 - The Interrelationship of 3D floor Characterisation strategies with Standardised 2nd Techniques

, Pages 197-220, Robert Ohlsson, Bengt Goran Rosén, John Westberg
9 - purposes of Numerical Parameters and Filtration

, Pages 221-247, Liam Blunt, Xiangqian Jiang
10 - performance and Characterisation of Textured Sheet metal Products

, Pages 249-305, Micheal Vermeiden, Henrik Hobleke
11 - Characterisation of car Engine Bore functionality utilizing 3D floor Metrology

, Pages 307-322, Stefan Brinkman, Horst Bodschwinna
12 - floor Texture wisdom aid — ISM

, Pages 325-337, Robert Ohlsson, Bengt Goran Rosén
13 - destiny advancements in floor Metrology

, Pages 339-347, Liam Blunt, Xiangqian Jiang, Paul J. Scott

, Pages 349-355

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Additional info for Advanced Techniques for Assessment Surface Topography. Development of a Basis for 3D Surface Texture Standards “SURFSTAND”

Sample text

But patterns of features, such as hills and pits and the relationships between them. Pattern analysis assesses a surface in the same way. By detecting features and the relationships between them it can characterise the patterns in surface texture. Parameters that characterise surface features and their relationships are here termed feature parameters. When process or functional diagnostics are required, field parameters are very blunt instruments. A medical analogy is useful to illustrate this point.

J. (1982) Two-dimensional discrete properties of random surfaces, Phil. Trans. R. Soc. Lond. A 305, pp 441-468. Novel Areal Characterisation Techniques [19] Bleau, A. J. (2000) [20] Image understanding, 77, pp 317-3 70. Barr6, F. and Lopez, J. , vol 40, pp 1171-1184. Vincent, L. and Soille, P. (1991) 61 Watershed-based segmentation and region merging, Computer Vision and Watershed lines and catchment basins: a new 3D-motif method, Int J. Mech. Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Trans, Pattern Anal.

18 Indications of the parameters of the index and the material area ratio A series of tests on engineering surfaces, such as ground, honed, lapped, and EDM surfaces have been carried out in order to verify the ability of the index family to characterise functional properties of the 3D surfaces. 19-21 a, obtained from these experiments of engineering surfaces, the results seem ambiguous. The values of index parameters are similar to each other whether different types of surfaces or different roughness levels are considered.

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