Design of a fuzzy controller for the adaptive control of WEDM process

The benefits of using instantaneous energy to monitor the transient state of the wire EDM process

“…For many researchers, sparking frequency has been the most valuable state parameter of the WEDM process for realizing adaptive methods, such as wire rupture prevention, workpiece height estimation, and adaptive fuzzy control for stable machining [7][8][9][10][11]. However, experiments in this research show that the machining state of the WEDM process may even become unstable without sparking frequency variation that is known as a dominant symptom of the instability of the gap condition.…”

Prediction and control of surface roughness in CNC lathe using artificial neural network

“…In their work, surface finish could be predicted with a reasonable degree of accuracy by taking the acceleration of radial vibration of the tool holder as a feedback. Liao and Woo (2000) explained the design of a fuzzy controller for the control of the wire electrical discharge machining process. As compared with a conventional control schema, the developed control strategy results were very satisfactory.…”

Wire EDM Monitoring for Zero-defect Manufacturing based on Advanced Sensor Signal Processing

“…A variation of the sparking frequency could help identify abnormal conditions before the occurrence of events such as short circuits [8], therefore the average sparking frequency was extracted as the mean of the sparking frequencies measured on a number of time periods. The sparking frequency was calculated on the basis of the voltage signal data as the total number of pulses divided by the time interval of the signal (10 ms).…”

“…2). In the literature, some methods to use the ignition delay time as a process sensing parameter have been proposed [7,8,11]. However, the voltage signals acquired during the WEDM process under study are much different from the theoretical one, as the plateau shown in Fig.…”