Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel

In the present research, two neuro-fuzzy models and a neural network model are presented for predictions of material removal rate (MRR), tool wear rate (TWR), and radial overcut (G) in die sinking electrical discharge machining (EDM) process for American Iron and Steel Institute D2 tool steel with c...

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Veröffentlicht in:	International journal of advanced manufacturing technology 2010-09, Vol.50 (5-8), p.591-610
Hauptverfasser:	Pradhan, Mohan Kumar, Biswas, Chandan Kumar
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Chromium molybdenum vanadium steels Convergence Die sinking EDM electrodes Electric discharge machining Factorial design Fuzzy logic Material removal rate (machining) Neural networks Predictions Pulse duration Tool steels Tool wear Variance analysis Wear rate
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