Experimental study on wire breakage detection by acoustic emission

This paper experimentally investigated wire breakage detection in a steel cable by acoustic emission(AE)waveform.In the experiments,the attenuation laws of waveform amplitudes were discussed based on stress wave propagation in the wire,which was generated by kNocking and wire breakage.Then the wave velocity was calculated based on the reach time of the stress wave from each sensor.Finally,based on the waveform attenuation laws and the linear position method,the amplitude and energy of the source were confirmed through the measured waveform to identify the source category.The experimental results illustrated that the stress wave from different sources has a different frequency spectrum,and the amplitude attenuation factor varied with the stress wave frequency;high frequency waves had a greater attenuation factor.Compared with the other source,the wire breakage source contained a much higher energy,and thus,the wire breakage signal can be distinguished from the other source by comparing the non-attenuation energy at the source position.

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining(EDM)process control.Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures.Such process interruptions are undesirable because they affect cost efficiency,surface quality,and process sustainability.The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear.Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images.In the proposed method,the images are preprocessed and enhanced to obtain a binary image that is used to compute the wire wear ratio(WWR).The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time,servo voltage,and wire feed rate.The algorithm successfully predicted wire breakage events.In addition,the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits,thereby preventing wire breakage interruptions.

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

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.A n 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.