Wire electrical discharge machining (WEDM) is investigated in the perspective of zero-defect manufacturing with the scope to detect anomalous process conditions leading to typical defects generated during WEDM, i.e. the occurrence of lines and marks on the resulting workpiece surface. A multiple sensor monitoring system is employed to acquire high sampling rate sensorial data relative to signals of voltage and current in the gap between workpiece and wire electrode. An advanced signal processing methodology is implemented to extract and select the most relevant features useful to identify the undesired process conditions through a cognitive pattern recognition paradigm.
Caggiano, A., Perez, R., Segreto, T., Teti, R., Xirouchakis, P. (2016). Advanced Sensor Signal Feature Extraction and Pattern Recognition for Wire EDM Process Monitoring. In Procedia CIRP (pp.34-39). SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS : Elsevier B.V. [10.1016/j.procir.2016.02.181].
Advanced Sensor Signal Feature Extraction and Pattern Recognition for Wire EDM Process Monitoring
Caggiano, Alessandra
;
2016-01-01
Abstract
Wire electrical discharge machining (WEDM) is investigated in the perspective of zero-defect manufacturing with the scope to detect anomalous process conditions leading to typical defects generated during WEDM, i.e. the occurrence of lines and marks on the resulting workpiece surface. A multiple sensor monitoring system is employed to acquire high sampling rate sensorial data relative to signals of voltage and current in the gap between workpiece and wire electrode. An advanced signal processing methodology is implemented to extract and select the most relevant features useful to identify the undesired process conditions through a cognitive pattern recognition paradigm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
