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Characterisation of Areal Surface Texture

Richard Leach
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TLDR
In this article, the areal field parameters and areal feature parameters were compared to solar cell efficiency and the mechanical bond strength for copper on glass plating applications, using laser-structured cams and conrods.
Abstract
Introduction to surface topography.- The areal field parameters.- The areal feature parameters.- Areal filtering methods.- Areal form removal.- Areal fractal methods.- Choosing the appropriate parameter.- Characterization of individual areal features.- Multi-scale signature of surface topography.- Correlation of areal surface texture parameters to solar cell efficiency.- Characterisation of cylinder liner honing textures for production control.- Characterization of the mechanical bond strength for copper on glass plating applications.- Inspection of laser structured cams and conrods.- Road surfaces.

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Citations
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Stereometric characterization of kefir microbial films associated with Maytenus rigida extract

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Designing indices to measure surface roughness based on the color distribution statistical matrix (CDSM)

TL;DR: A color image-based indices design and evaluation method that can quantitatively and comprehensively characterize the performance of different indices is proposed and designed.
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Modeling of tooth surface topography in continuous generating grinding based on measured topography of grinding worm

TL;DR: Based on the measured topography of the grinding worm, a new approach to modeling the tooth surface topography in continuous generating grinding is studied in this paper, where the replicated surface is measured and reconstructed to obtain the real topography.
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Multi-sensor measurements of titanium alloy surface texture formed at subsequent operations of precision machining process

TL;DR: In this paper, the surface texture obtained after each operation of machining process was measured using a white light interference microscopy, a scanning electron microscopy and an atomic force microscopy.
References
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Some methods for classification and analysis of multivariate observations

TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
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An introduction to ROC analysis

TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
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Neural Networks for Pattern Recognition

TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
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