R.J. Pike

Bio: R.J. Pike is an academic researcher. The author has contributed to research in topics: Surface metrology. The author has an hindex of 1, co-authored 1 publications receiving 123 citations.

Quantitative characterization of micro-topography a bibliography of industrial surface metrology

Abstract: 3

Surface roughness measurement by means of speckle wavelength decorrelation

Abstract: Abstract We exploit speckle wavelength decorrelation techniques for the characterization of surface roughness up to a few microns. The experimental setup includes an argon laser, a grating, two photodetectors, and a commercial cross correlator. Experimental results are compared with data obtained with a stylus instrument. Fair agreement is observed.

Digital Terrain Modeling and Industrial Surface Metrology: Converging Realms

Abstract: Digital terrain modeling has a micro- and nanoscale counterpart in surface metrology, the numerical characterization of industrial surfaces. Instrumentation in semiconductor manufacturing and other high-technology fields can now contour surface irregularities down to the atomic scale. Surface metrology has been revolutionized by its ability to manipulate square-grid height matrices that are analogous to the digital elevation models (DEMs) used in physical geography. Because the shaping of industrial surfaces is a spatial process, the same concepts of analytical cartography that represent ground-surface form in geography evolved independently in metrology. The surface topography of manufactured components, exemplified here by automobile-engine cylinders, is routinely modeled by variogram analysis, relief shading, and most other techniques of parameterization and visualization familiar to geography. This article introduces industrial surface-metrology, examines the field in the context of terrain modeling a...

Interdependence of Amplitude Roughness Parameters on Rough Gaussian Surfaces

Abstract: According to international standards, the topography and the quality of machined surfaces can be characterized simultaneously by more than 70 roughness parameters. Despite the increased accuracy of topography measurements by modern instruments, the gained information about the 3D surface is still not well understood. The fact that machined surfaces are in general of Gaussian height distribution motivated the authors to study the interdependence of the standardized amplitude roughness parameters of computer-generated random rough (Gaussian) surfaces. In this contribution, these rough surfaces are created by solving numerically a Langevin-type stochastic differential equation for a defined random process, namely a Gaussian one. This numerical scheme provides rough surfaces of pre-defined statistical features, e.g., given standard deviation and correlation length. The numerical analysis of 17 standardized amplitude roughness parameters collected from 90000 computer-generated rough surfaces revealed so far undetected interdependencies among some of these parameters, namely the results show a strong linear relation between 12 amplitude roughness parameters.

Digital terrain modelling and industrial surface metrology — converging crafts

Abstract: Quantitative characterisation of surface form, increasingly from digital 3-D height data, is cross-disciplinary and can be applied at any scale. Thus, separation of industrial-surface metrology from its Earth-science counterpart, (digital) terrain modelling, is artificial. Their growing convergence presents an opportunity to develop in surface morphometry a unified approach to surface representation. This paper introduces terrain modelling and compares it with metrology, noting their differences and similarities. Examples of potential redundancy among parameters illustrate one of the many issues common to both disciplines.