Wire EDM failure prediction and process control based on sensor fusion and pulse train analysis

The study aims to develop a neural network classification model predict machining failures during wire electric discharge machining. Also, process control algorithm retunes the parameters based on remaining useful time before failure. In proposed methodology, an artificial (ANN) classifier receives four in-process characteristics as input. These extracted features are energy, spark frequency, open ratio, and short circuit ratio. Output classes labeled normal machining, breakage, absence. One hundred eight experiments were conducted according full factorial design train model, with 90% accuracy.Parallelly, another trained ANN predicts failure, which retuned restore stability. was successful in ensuring continuous failure-free