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Cited 3 time in webofscience Cited 10 time in scopus
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Estimation of machinability performance in wire-EDM on titanium alloy using neural networks

Authors
Paturi, Uma Maheshwera ReddyCheruku, SuryapavanSalike, SritejaPasunuri, Venkat Phani KumarReddy, N. S.
Issue Date
4-Jul-2022
Publisher
TAYLOR & FRANCIS INC
Keywords
WEDM; Ti-6Al-4V; roughness; speed; width; MRR; experimental; ANN
Citation
MATERIALS AND MANUFACTURING PROCESSES, v.37, no.9, pp.1073 - 1084
Indexed
SCIE
SCOPUS
Journal Title
MATERIALS AND MANUFACTURING PROCESSES
Volume
37
Number
9
Start Page
1073
End Page
1084
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/1060
DOI
10.1080/10426914.2022.2030875
ISSN
1042-6914
Abstract
The impact of process factors on wire-cut electrical discharge machining (WEDM) performance is complex and nonlinear. In the present work, initially, the WEDM tests were conducted on titanium alloy (Ti-6Al-4V) with eight input factors and four machinability performance parameters. Later, an artificial neural network (ANN) model was established to estimate the WEDM performance. The ANN model with 8-5-5-4 architecture produced a least mean squared error (MSE) and average prediction error (AE) for both training and test data sets. The precision of the ANN model was assessed by relating model predictions with the experimental values. The combined effect of WEDM variables on the machinability performance was illustrated with the help of visual graphs. The R-value (correlation coefficient) of 0.9995 among WEDM test values and ANN estimated values shows the robustness of the developed ANN model in establishing the link between WEDM process factors and machinability parameters. The proposed model helps in minimizing the time for fixing the process parameter values, thereby increasing production and process efficiency.
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공과대학 (나노신소재공학부금속재료공학전공)
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