Prediction and analysis of process failures by ANN classification during wire-EDM of Inconel 718

Abstract

Wire breakages and spark absence are two typical machining failures that occur during wire electric discharge machining (wire-EDM), if appropriate parameter settings are not maintained. Even after several attempts to optimize the process, machining failures cannot be eliminated completely. An offline classification model is presented herein to predict machining failures. The aim of the current study is to develop a multiclass classification model using an artificial neural network (ANN). The training dataset comprises 81 full factorial experiments with three levels of pulse-on time, pulse-off time, servo voltage, and wire feed rate as input parameters. The classes are labeled as normal machining, spark absence, and wire breakage. The model accuracy is tested by conducting 20 confirmation experiments, and the model is discovered to be 95% accurate in classifying the machining outcomes. The effects of process parameters on the process failures are discussed and analyzed. A microstructural analysis of the machined surface and worn wire surface is conducted. The developed model proved to be an easy and fast solution for verifying and eliminating process failures.

Keywords

Wire electric discharge machining (wire-EDM) / Process failure / Spark absence / Wire breakage / Artificial neural network (ANN) classification / Failure prediction

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