Generation of reference data of 3D surface texture using the non-causal 2D AR model

Uchidate, Michimasa; Shimizu, Tomoharu; Iwabuchi, Akira; Yanagi, K.

doi:10.1016/j.wear.2004.05.019

Cited by 34 publications

(22 citation statements)

References 9 publications

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“…Numerical data generated with a non-causal 2-D auto-regression (AR) model (11) were used in order to obtain data that satisfy the assumptions for applying the estimation described in Section 2.1. This model is expressed as follows:…”

Section: Datamentioning

confidence: 99%

“…β is the correlation distance and ω is a parameter called the "correlation power index" (11) . Data sets that have specified β and ω can be generated numerically by applying the algorithm shown in Ref.…”

Section: Datamentioning

confidence: 99%

“…Data sets that have specified β and ω can be generated numerically by applying the algorithm shown in Ref. (11).…”

Section: Datamentioning

confidence: 99%

“…On the other hand, computationally generated data would be preferable since they are free of undesirable components. Also, by using a computer generated data based on a stochastic model, it is possible to assess the result more systematically since random surfaces with various properties can be obtained (11) .…”

Section: Introductionmentioning

confidence: 99%

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Research on the Validity of using Nayak's Theory for Summit Parameters of Discrete Isotropic Gaussian Surfaces

Uchidate

Iwabuchi

Kikuchi

et al. 2009

JAMDSM

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In this paper, with the aim of more appropriately characterizing surface topography for tribology, the validity of Nayak's theory is assessed through a comparison with direct calculations. The summit parameters examined in this study are the areal density, mean radius of curvature, and standard deviation of height. Functions such as the probability density of summit height and summit height vs. mean radius of curvature are also discussed. Purely homogeneous, isotropic and Gaussian distributed surfaces generated with a stochastic model are used to avoid unpredictable components such as noise and local irregularities. Overall, the estimation showed good qualitative agreement with the direct calculation. The relative discrepancies range from −10 to 20% for density, −20 to −8% for curvature, and −2 to 10% for height.

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Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

“…Data sets that have specified β and ω can be generated numerically by applying the algorithm shown in Ref. (11).…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on the Validity of using Nayak's Theory for Summit Parameters of Discrete Isotropic Gaussian Surfaces

Uchidate

Iwabuchi

Kikuchi

et al. 2009

JAMDSM

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“…The calculated time series model coefficients from few sample data can serve as input for the generation of artificial surfaces that are then used for the training of classifiers for an ANN (Figure 1). Auto-regressive (AR), moving average (MA) and auto-regressive moving average (ARMA) models have been previously applied for the characterization of rough surfaces and the statistical description of roughness (see e.g., [11,12,25]). We propose an approach based on the ARMAsel model presented by Broersen [26] which can find the best possible time series model for an accurate description of a measured surface.…”

Section: Introductionmentioning

confidence: 99%

Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation

et al. 2017

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Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN) are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive-as e.g., measuring surface topography. Thus, generation of artificial (simulated) data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted in a way that a distinction of these surfaces is possible by an ANN. The ARMAsel approach is proven to provide the relevant stochastic information for the training of the ANN with artificially generated lapped, reamed, ground, horizontally milled, milled and turned surface profiles.

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Surface topography measurement instrumentation

Leach¹

2010

Fundamental Principles of Engineering Nanometrology

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Generation of reference data of 3D surface texture using the non-causal 2D AR model

Cited by 34 publications

References 9 publications

Research on the Validity of using Nayak's Theory for Summit Parameters of Discrete Isotropic Gaussian Surfaces

Research on the Validity of using Nayak's Theory for Summit Parameters of Discrete Isotropic Gaussian Surfaces

Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation

Surface topography measurement instrumentation

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