Machine Learning Based Classification and Analysis of Wire-EDM Discharge Pulses
Machine Learning Based Classification and Analysis of Wire-EDM Discharge Pulses
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DOI：
10.1109/nanoman-aets56035.2022.10119466
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
P. Abhilash;D. Chakradhar;X. Luo
中科院分区：
文献类型：
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作者：
P. Abhilash;D. Chakradhar;X. Luo
电火花线切割加工 (wire-EDM) 工艺由于其材料去除的非接触性质，比传统加工方法具有巨大的潜力。然而，频繁出现的意外加工故障（例如断丝）会对流程的生产率、可持续性和效率产生负面影响。在这种背景下，通过在线状态监测来提高过程效率的范围很广。 EDM 状态监测的一个突出方面是放电脉冲辨别。目前使用的基于阈值的方法精度较低并且依赖于操作者的经验。在本研究中，提出了一种基于机器学习（ML）的脉冲分类，该分类基于提取的放电特征。这些特征是从在线放电加工操作期间从加工区域收集的原始电压和电流传感器信号中提取的。在各种 ML 模型中，人工神经网络 (ANN) 分类器的最大预测精度为 98%。此外，还研究了不同放电脉冲对生产率、表面光洁度和加工故障的影响。研究发现，如果短路和电弧放电占脉冲周期的 80% 以上，就会导致断线故障。此外，短时火花和电弧火花会显着增加表面粗糙度，最高可达 70%。
Wire electrical discharge machining (wire-EDM) process is having immense potential over conventional machining methods due to its non-contact nature of material removal. However, frequent and unanticipated machining failures like wire breakages negatively affect the productivity, sustainability and efficiency of the process. In this context, there is a wide scope to improve the process efficiency through online condition monitoring. A prominent aspect of EDM condition monitoring is discharge pulse discrimination. The threshold based methods which are currently being used has low accuracy and is reliant on operator's experience. In this study, a machine learning (ML) based pulse classification based on the extracted discharge characteristics is proposed. The features are extracted from the raw voltage and current senor signals collected from the machining zone during the wire EDM operation. Among the various ML models, Artificial Neural Network (ANN) classifier is found to have the maximum prediction accuracy of 98 %. Also, the effects of different discharge pulses on the productivity, surface finish and machining failures are investigated. The short circuit and arc discharges are found to cause wire breakage failure if they predominate the pulse cycle by more than 80 %. Also, short and arc sparks increase the surface roughness significantly, by up to 70 %.