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.
Advances in Manufacturing – Springer Journals
Published: Nov 29, 2017
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