Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis

Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire... The current study investigates the behavior of wire electric discharge machining (WEDM) of the super alloy Udimet-L605 by employing sophisticated machine learning approaches. The experimental work was designed on the basis of the Taguchi orthogonal L27 array, considering six explanatory variables and evaluating their influences on the cutting speed, wire wear ratio (WWR), and dimensional deviation (DD). A support vector machine (SVM) algorithm using a normalized poly-kernel and a radial-basis flow kernel is recommended for modeling the wire electric discharge machining process. The grey relational analysis (GRA) approach was utilized to obtain the optimal combination of process variables simultaneously, providing the desirable outcome for the cutting speed, WWR, and DD. Scanning electron microscope and energy dispersive X-ray analyses of the samples were performed for the confirmation of the results. An SVM based on the radial-basis kernel model dominated the normalized poly-kernel model. The optimal combination of process variables for a mutually desirable outcome for the cutting speed, WWR, and DD was determined as T on1, T off2, I P1, WT3, SV1, and WF3. The pulse-on time is the significant variable influencing the cutting speed, WWR, and DD. The largest percentage of copper (8.66%) was observed at the highest cutting speed setting of the machine compared to 7.05% of copper at the low cutting speed setting of the machine. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Manufacturing Springer Journals

Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis

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Publisher
Shanghai University
Copyright
Copyright © 2017 by Shanghai University and Springer-Verlag GmbH Germany
Subject
Engineering; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics; Nanotechnology and Microengineering
ISSN
2095-3127
eISSN
2195-3597
D.O.I.
10.1007/s40436-017-0192-7
Publisher site
See Article on Publisher Site

Abstract

The current study investigates the behavior of wire electric discharge machining (WEDM) of the super alloy Udimet-L605 by employing sophisticated machine learning approaches. The experimental work was designed on the basis of the Taguchi orthogonal L27 array, considering six explanatory variables and evaluating their influences on the cutting speed, wire wear ratio (WWR), and dimensional deviation (DD). A support vector machine (SVM) algorithm using a normalized poly-kernel and a radial-basis flow kernel is recommended for modeling the wire electric discharge machining process. The grey relational analysis (GRA) approach was utilized to obtain the optimal combination of process variables simultaneously, providing the desirable outcome for the cutting speed, WWR, and DD. Scanning electron microscope and energy dispersive X-ray analyses of the samples were performed for the confirmation of the results. An SVM based on the radial-basis kernel model dominated the normalized poly-kernel model. The optimal combination of process variables for a mutually desirable outcome for the cutting speed, WWR, and DD was determined as T on1, T off2, I P1, WT3, SV1, and WF3. The pulse-on time is the significant variable influencing the cutting speed, WWR, and DD. The largest percentage of copper (8.66%) was observed at the highest cutting speed setting of the machine compared to 7.05% of copper at the low cutting speed setting of the machine.

Journal

Advances in ManufacturingSpringer Journals

Published: Nov 29, 2017

References

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