Monitoring the E.D.M. process by radio signals

Bhattacharyya, S. K.; ElMenshawy, Mena

doi:10.1080/00207547808930027

Cited by 36 publications

(11 citation statements)

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“…Han et al [23] proposed a transistor-based constant pulse generator to monitor the gap by using the average breakdown delay time. Bhattacharyya et al [24] adopted the radio frequency signal (RFS) detection method to monitor the gap distance according to different discharge gap RFS characteristics corresponding to different gap discharge state. Other methods for identifying EDM gap distance include wavelet transform detection [25], fuzzy identification [26], and neural network identification [27,28].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Interelectrode Gap in Electric Discharge Machining and Minimum Variance Self-Tuning Control of Interelectrode Gap

Xin

Gao

et al. 2020

Mathematical Problems in Engineering

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In the electric discharge machining system, the determination of the gap between the anode and the cathode is a difficult point of this kind of machining approach. An accurate mathematical model of interelectrode gap is obtained, and the precise control of the gap is achieved on this basis. In this paper, based on the example of discharge machining of P-type single crystal Si, the theoretical analysis proved that the discharge channel can be equivalent to pure resistance, and the physical model of the interelectrode gap and voltage and current was established. The order and parameters of the EDM system model were determined by adopting the system identification theory. We designed the minimum variance self-correcting controller to accurately control the interelectrode gap in combination with the actual machining process. Experimental results show that the interelectrode gap model can correctly reflect the interelectrode gap in the actual machining process; the minimum variance self-correcting controller eliminates the short circuit phenomenon during processing and can stably track different desired gaps; the material removal rate and the surface roughness decrease with the increase of the interelectrode gap.

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Section: Introductionmentioning

confidence: 99%

Modeling of Interelectrode Gap in Electric Discharge Machining and Minimum Variance Self-Tuning Control of Interelectrode Gap

Xin

Gao

et al. 2020

Mathematical Problems in Engineering

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“…It has also been shown that the noise waveform of discharges between switch contacts is influenced by the choice of material [21] and electrode area [22]. In the late 1970s, early work showed that it was possible to use RF transmissions to distinguish between open-circuit, spark, arc, and short-circuit conditions in macroscale serial-mode EDM [23], [24]. At the microscale, particularly for batch mode, process monitoring is even more critical.…”

Section: B Sensing Debris-dominated Machiningmentioning

confidence: 98%

Wireless Monitoring of Workpiece Material Transitions and Debris Accumulation in Micro-Electro-Discharge Machining

Richardson

Gianchandani

2010

J. Microelectromech. Syst.

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Wireless signals are inherently generated with each discharge in micro-electro-discharge machining (μEDM), allowing direct observation of discharge quality. While traditional methods of monitoring machining quality rely on electrical characteristics at the discharge supply terminals, the wireless method provides more accurate information because it is less affected by electrical parasitics in the supply loop and by spatial averaging. The depth location of a metal-metal interface can be distinguished in the wireless signal. This is useful for determining the stop depth in certain processes. For example, in machining through samples of stainless steel into an electroplated copper backing layer, the metal transition is identified by a 10-dBm change in wireless-signal strength for the 300-350-MHz band and a 5-dBm average change across the full 1-GHz bandwidth. As debris accumulate in the discharge gaps, shifts in the wireless spectra can also indicate spurious discharges that could damage the workpiece and tool. For example, when copper micromachining becomes debris dominated, the 800-850-MHz band drops 4 dBm in signal strength, with a 2.2-dBm average drop across the full 1-GHz bandwidth.[ 2009-0109]Index Terms-Batch-mode micromachining, process monitoring, spurious discharges, stainless-steel machining, wireless feedback control.

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“…In order to over come from such circumstances a skilled operator is required to take a corrective action to stable the machine by adopting proper corrective measures. Further different online controllers were developed; polynomial function based Abductive network [89,94], pulse discriminating type analyser [95], radio frequency analyser [96,97], neutral network base [90,98], fuzzy base pulse discriminator system [99,100] and digital signal procesing base [101]. These controllers identifies EDM process pulses and discriminates it and take proper corrective measures to stabilise the machine.…”

Section: Pulse Discriminatingmentioning

confidence: 99%

A Brief Review of Die Sinking Electrical Discharging Machining Process towards Automation

Pawade¹,

Banwait²

2013

AJME

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Electrical Discharge Machine (EDM) is especially used for the manufacturing of 3-D complex geometry and hard material parts that are extremely difficult-to-machine by conventional machining processes. In this paper authors have reviewed the research work carried out in the development of die-sinking EDM within the past decades for the improvement of machining characteristics such as Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Ratio (TWR). In this review various techniques reported by EDM researchers for improving the machining characteristics have been categorized as process parameters optimization, multi spark technique, powder mixed EDM, servo control system and pulse discriminating. At the end, flexible machine controller is suggested for Die Sinking EDM to enhance the machining characteristics and to achieve high-level automation. Thus, die sinking EDM can be integrated with Computer Integrated Manufacturing (CIM) environment as a need of agile manufacturing systems.

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Monitoring the E.D.M. process by radio signals

Cited by 36 publications

References 1 publication

Modeling of Interelectrode Gap in Electric Discharge Machining and Minimum Variance Self-Tuning Control of Interelectrode Gap

Modeling of Interelectrode Gap in Electric Discharge Machining and Minimum Variance Self-Tuning Control of Interelectrode Gap

Wireless Monitoring of Workpiece Material Transitions and Debris Accumulation in Micro-Electro-Discharge Machining

A Brief Review of Die Sinking Electrical Discharging Machining Process towards Automation

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