Abstract
During wire electrical discharge machining (WEDM), wire rupture may deteriorate workpieces’ machined surfaces and increase the processing time. However, only a few referenced papers focused on wire rupture during past decades because of its complexity. In this research, machine learning (ML) technique was applied to analyze the relationship between manufacturing parameters and the chance of wire rupture. Three parameters, including gap voltage (GV), feed rate (FR), and water resistance (WR), were considered as training features, and a total of 298 sets were used to train an artificial neural network (ANN). The results show that the prediction accuracy of wire rupture for 10 s in advance is above 85%. This research developed a new method to apply the real-time predict wire rupture and is faster, more accurate than prior research. Besides, this method is extendable for future measured data when the usable sensor data are increasing.
Highlights
Nowadays, wire electrical discharge machining (WEDM) is one of the widely used non-conventional machining processes
Machine Learning (ML) technique was applied to analyze the relationship between manufacturing parameters and the chance of wire rupture
This paper shows that the ML algorithm can be used to predict wire rupture for WEDM
Summary
It may be applied to cut electrically conductive materials and complicated machining shapes that traditional machining processes cannot produce [1,2]. The risk of wire breakage has undermined the full potential of the WEDM, drastically reducing the efficiency and accuracy of the process. Different factors lead to wire breakages, like high wire tension, electrical discharge impact, thermal load, and hightemperature influence the wire strength and, wire rupture [9]. These phenomena come from the machining parameters, so the relationship between machining parameters and the possibility of wire rupture can be considered a complicated nonlinear problem
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