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Development of surface roughness optimisation and prediction for the process of wire electro-discharge grinding

Rees, Andrew, Brousseau, Emmanuel Bruno Jean Paul, Dimov, Stefan Simeonov, Bigot, Samuel and Griffiths, Christian Andrew 2013. Development of surface roughness optimisation and prediction for the process of wire electro-discharge grinding. International Journal of Advanced Manufacturing Technology 64 (9-12) , pp. 1395-1410. 10.1007/s00170-012-4110-7

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Abstract

This paper investigates the technological capabilities of a hybrid micro machining process for performing wire electro-discharge grinding (WEDG). In particular, micro wire electrical discharge machining (μWEDM) is employed in combination with a rotating submergible spindle to perform WEDG. In this research, first a machining strategy for workpiece preparation is presented. Then, the effects of different machining setup parameters on the achievable surface finish after WEDG are investigated. In particular, an experimental study was conducted to identify the most statistically significant setup parameters for performing the main cut that affect the resulting surface quality. A signal-to-noise (S/N) ratio analysis was conducted to optimise the technological parameters for performing WEDG. By modifying the discharge energy for main cuts when performing WEDG surface finish comparable to that of μWEDM can be achieved. In addition, a simple and cost-effective method for on-the-machine estimation of resulting surface roughness is proposed. Especially, by applying inductive learning a surface roughness prediction model for WEDG can be generate based on data acquired by monitoring on-line the process.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Engineering
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: T Technology > TJ Mechanical engineering and machinery
Uncontrolled Keywords: Micro EDM, Micro WEDM, WEDG, Inductive learning
Publisher: Springer
ISSN: 0268-3768
Funders: EU FP7
Last Modified: 10 Dec 2019 23:08
URI: http://orca.cf.ac.uk/id/eprint/36714

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