Abstract
The main purpose of this research is to check the relative importance of methods fuzzy-logic and back-propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434?µm.
Highlights
The wire-cut electric discharge machining is the machine of unique type which employed the diminutive size of 0.05 mm to 0.3 mm diameter wire for separating the infant material from the parental plate of material [1]
This paper examined the change in the surface characteristics of Udimet-L605 after wire electric discharge machine (WEDM) on Udimet-L605
It has been evaluated that most of the predicted value provided by fuzzy model and BP-artificial neural network (ANN) model lies in the range of Æ05% error line which shows the consistency of both model
Summary
Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy.
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