Identification of Surface Characteristics from Large Samples-Reference-Cited by-同舟云学术

Identification of Surface Characteristics from Large Samples

Published:1992-07 Issue:4 Volume:206 Page:275-284
ISSN:0954-4062
Container-title:Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Author:

Kovacevic R¹,Zhang Y M¹²

Affiliation:

1. Centre for Robotics and Manufacturing Systems and Department of Mechanical Engineering, University of Kentucky, USA

2. Yu Ming Zhang is a faculty member at the Harbin Institute of Technology, People's Republic of China.

Abstract

Surface roughness characteristics have been modelled by autoregressive moving average (ARMA) models. Frequently, extra-large samples from the surface are available. Due to the non-linearity and the computational burden dependence on sample size, the available data can not be sufficiently utilized to fit ARMA models in most cases. In an attempt to sufficiently employ the available data, an innovative ARMA identification approach is presented. The computational burden of this approach is nearly independent of the sample size. The accuracy ratio between the present approach and the non-linear least squares algorithm is determined. Both simulation and application have been conducted to confirm its effectiveness.

Publisher

SAGE Publications

Subject

Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1243/PIME_PROC_1992_206_127_02

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1. Optimum Sampling Interval for Ra Roughness Measurement

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