Process parameters selection for friction surfacing applications using intelligent decision support
Process parameters selection for friction surfacing applications using intelligent decision support
Friction surfacing is an advanced manufacturing process, which has been successfully developed and commercialised over the past decade. The process is used for corrosion and wear resistant coatings and for reclamation of worn engineering components. At present the selection of process parameters for new coating materials or substrate geometries is by experiment requiring lengthy development work. The major requirement is for flexibility to enable rapid changes of process parameters in order to develop new applications, with variations of materials and geometries in a cost effective and reliable manner. Further improvement requires development of appropriate mathematical models of the process, which will facilitate the introduction of optimisation techniques for efficient experimental work as well as the introduction of real-time feedback adaptive control. This paper considers the use of combined artificial intelligence and modelling techniques. It includes a new frame of a neurofuzzy-model based decision support system—FricExpert, which is aimed at speeding up the parameter selection process and to assist in obtaining values for cost effective development. Derived models can then be readily used for optimisation techniques, discussed in our earlier work.
surfaces and interfaces, metals and alloys, materials engineering
27-32
Vitanov, V.I.
93337e2a-78cd-43b6-b45e-07abf4ae0cbd
Voutchkov, I.I.
16640210-6d07-49cc-aebd-28bf89c7ac27
2005
Vitanov, V.I.
93337e2a-78cd-43b6-b45e-07abf4ae0cbd
Voutchkov, I.I.
16640210-6d07-49cc-aebd-28bf89c7ac27
Vitanov, V.I. and Voutchkov, I.I.
(2005)
Process parameters selection for friction surfacing applications using intelligent decision support.
Journal of Materials Processing Technology, 159 (1), .
(doi:10.1016/j.jmatprotec.2003.11.006).
Abstract
Friction surfacing is an advanced manufacturing process, which has been successfully developed and commercialised over the past decade. The process is used for corrosion and wear resistant coatings and for reclamation of worn engineering components. At present the selection of process parameters for new coating materials or substrate geometries is by experiment requiring lengthy development work. The major requirement is for flexibility to enable rapid changes of process parameters in order to develop new applications, with variations of materials and geometries in a cost effective and reliable manner. Further improvement requires development of appropriate mathematical models of the process, which will facilitate the introduction of optimisation techniques for efficient experimental work as well as the introduction of real-time feedback adaptive control. This paper considers the use of combined artificial intelligence and modelling techniques. It includes a new frame of a neurofuzzy-model based decision support system—FricExpert, which is aimed at speeding up the parameter selection process and to assist in obtaining values for cost effective development. Derived models can then be readily used for optimisation techniques, discussed in our earlier work.
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Published date: 2005
Keywords:
surfaces and interfaces, metals and alloys, materials engineering
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Local EPrints ID: 23303
URI: http://eprints.soton.ac.uk/id/eprint/23303
ISSN: 0924-0136
PURE UUID: 9ac5a1bd-a80c-4122-aa79-760eb30a8115
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Date deposited: 14 Mar 2006
Last modified: 15 Mar 2024 06:46
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Author:
V.I. Vitanov
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