Development of an On-Line Defect Detection System for EDM Process

In the electrical discharge machining process, preliminary research has been able to effectively estimate machining accuracy in response to its long machining history and high discharge frequency characteristics. However, when processing abnormalities occur, it is difficult to identify them since the electrical discharge process contains multiple processing parameters, which increases the cost of repair or loss afterwards. Therefore, the question concerning how to monitor the abnormality of the discharge process in real time represents the main purpose of this research. This research develops an EDM process abnormal diagnosis system. First, the data are stored in a circular array to speed up the processing time, and the coefficient of variation feature is added, which has effectively extracted the abnormal characteristics. In terms of diagnostic methods, the composite voting model established by neural networks, random forests, and XGB-RF (extreme gradient boosting applying RF) can provide robust diagnostic results. Finally, through the Node-RED webpage and MQTT agreement, it can provide the ability to monitor machine abnormalities in real time. Through refinement and optimization of the previous research results, this study took the electrical discharge machining diamond grinding wheel as an example, and developed a warning that can be issued within 3 min when abnormalities (abnormal patterns such as polycrystalline diamond high protrusions) occur, with an accuracy of 93% and a false positive rate. The abnormal diagnosis ability is less than 0.2%. Therefore, the online abnormality monitoring system developed by this research institute will be able to provide online abnormality diagnosis for electrical discharge machining.

Automated summary

This study develops an EDM process abnormal diagnosis system that can extract and diagnose machining abnormalities using the coefficient of variation feature and a composite voting model. It provides real-time monitoring of machine abnormalities through an online monitoring system.