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- W2068880003 abstract "In Wire Electrical Discharge Machining (WEDM) the breakage of the cutting tool (the wire) reduces the process performance and the required accuracy. Previous works of authors showed that the behavior of the basic signals of the process (current and voltage) can be employed to detect degraded situations that can lead to wire breakage. In particular, different types of degraded behaviors in two commonly used workpiece thicknesses (50 and 100 mm) were identified. In order to achieve this objective, a set of virtual sensors were defined and constructed from the basic signals of the process. Although the types of degraded behaviors were common for the studied workpiece thicknesses, the thresholds achieved by the virtual measurements depended on these. At the sight of this conclusion, the main goal of this work is to detect the process degradation in different workpiece thicknesses using one unique empirical model. Since Artificial Neural Networks are appropriated for processes of stochastic and nonlinear nature, its use is investigated here in order to interpolate the instability trends for different workpiece thicknesses. Firstly, a comparative study performed to select the most appropriated configuration of the neural network is summarized. Secondly, the strategy applied to detect degraded situations in different workpiece thicknesses is presented. The results of this work show a satisfactory performance of the presented approach." @default.
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- W2068880003 date "2008-01-01" @default.
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- W2068880003 title "ANN for Interpolating Instability Trends in WEDM" @default.
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- W2068880003 doi "https://doi.org/10.3182/20080706-5-kr-1001.00376" @default.
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