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.

如果不保持适当的参数设置，则断线和无火花是在电火花线切割加工（wire-EDM）期间发生的两种典型的加工故障。即使经过几次尝试来优化过程，也无法完全消除加工失败。本文提出离线分类模型以预测加工故障。当前研究的目的是使用人工神经网络（ANN）开发多分类模型。训练数据集包含81个全因子实验，其中三个级别的脉冲接通时间，脉冲断开时间，伺服电压和送丝速率作为输入参数。这些类别被标记为正常加工，无火花和断线。通过进行20次确认实验来测试模型的准确性，在对加工结果进行分类时，发现该模型的准确率达到95％。讨论并分析了过程参数对过程故障的影响。进行了机加工表面和磨损线表面的微观结构分析。事实证明，开发的模型是验证和消除过程故障的便捷解决方案。