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An integrated approach to friction surfacing process optimisation

An integrated approach to friction surfacing process optimisation
An integrated approach to friction surfacing process optimisation
This paper discusses the procedures for data collection, management and optimisation of the friction surfacing process. Experimental set-up and characteristics of measuring equipment are found to match the requirements for accurate and unbiased data signals. The main friction surfacing parameters are identified and the first stage of the optimisation process is achieved by visually assessing the coatings and introducing the substrate speed vs. force map. The optimum values from this first stage forms a region around the middle of a trapezium-shaped area whose borders are found experimentally. Data collected for the second stage were analysed using the least squares method which were applied to find the coefficients of a second order regression model. Advantages of applying artificial intelligence methods to friction surfacing modelling are also described and the higher accuracy achieved using neural networks demonstrated.
Friction surfacing, Optimisation, Neural networks
0257-8972
26-33
Voutchkov, I.I.
16640210-6d07-49cc-aebd-28bf89c7ac27
Jaworski, B.
9caf2eae-9131-4254-b2f1-939c9624d3f9
Vitanov, V.I.
93337e2a-78cd-43b6-b45e-07abf4ae0cbd
Bedford, G.M.
0d37d977-3a31-4004-82e0-fd8964c4835c
Voutchkov, I.I.
16640210-6d07-49cc-aebd-28bf89c7ac27
Jaworski, B.
9caf2eae-9131-4254-b2f1-939c9624d3f9
Vitanov, V.I.
93337e2a-78cd-43b6-b45e-07abf4ae0cbd
Bedford, G.M.
0d37d977-3a31-4004-82e0-fd8964c4835c

Voutchkov, I.I., Jaworski, B., Vitanov, V.I. and Bedford, G.M. (2001) An integrated approach to friction surfacing process optimisation. Surface and Coatings Technology, 141 (1), 26-33. (doi:10.1016/S0257-8972(01)01127-6).

Record type: Article

Abstract

This paper discusses the procedures for data collection, management and optimisation of the friction surfacing process. Experimental set-up and characteristics of measuring equipment are found to match the requirements for accurate and unbiased data signals. The main friction surfacing parameters are identified and the first stage of the optimisation process is achieved by visually assessing the coatings and introducing the substrate speed vs. force map. The optimum values from this first stage forms a region around the middle of a trapezium-shaped area whose borders are found experimentally. Data collected for the second stage were analysed using the least squares method which were applied to find the coefficients of a second order regression model. Advantages of applying artificial intelligence methods to friction surfacing modelling are also described and the higher accuracy achieved using neural networks demonstrated.

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More information

Published date: 2001
Keywords: Friction surfacing, Optimisation, Neural networks

Identifiers

Local EPrints ID: 23304
URI: http://eprints.soton.ac.uk/id/eprint/23304
ISSN: 0257-8972
PURE UUID: 5a922ed2-08a6-4c11-941d-b4732f728979

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Date deposited: 21 Mar 2006
Last modified: 15 Mar 2024 06:46

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Contributors

Author: I.I. Voutchkov
Author: B. Jaworski
Author: V.I. Vitanov
Author: G.M. Bedford

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