Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/2907
Title: Application of a Taguchi-based neural network for forecasting and optimization of the surface roughness in a wire-electrical-discharge machining process
Authors: Kazancoglu, Yigit
Esme, Ugur
Kulekci, Mustafa Kemal
Kahraman, Funda
Samur, Ramazan
Akkurt, Adnan
Ipekci, Melih Turan
Keywords: WEDM
Taguchi-design method
neural network
surface roughness
Material Removal Rate
Multiobjective Optimization
Wedm Process
Parameters
Steel
Publisher: Inst Za Kovinske Materiale I In Tehnologie
Abstract: Wire-electrical-discharge machining (WEDM) is a modification of electro-discharge machining (EDM) and has been widely used for a long time for cutting punches and dies, shaped pockets and other machine parts on conductive materials. WEDM erodes workpiece materials by a series of discrete electrical sparks between the workpiece and an electrode flushed or immersed in a dielectric fluid. The WEDM process is particularly suitable for machining hard materials as well as complex shapes. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the surface roughness in a WEDM process. A back-propagation neural network (BPNN) was developed to predict the surface roughness. In the development of a predictive model, machining parameters of open-circuit voltage, pulse duration, wire speed and dielectric flushing pressure were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the surface roughness (R-a). Finally, the Taguchi approach was applied to determine the optimum levels of machining parameters.
URI: https://hdl.handle.net/20.500.14365/2907
ISSN: 1580-2949
1580-3414
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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