Models Of Electrical Discharge Machining Process Research Articles

Modeling of EDM Process Flushing Mechanism.

The study was performed to determine the optimum flushing condition for electrical discharge machining (EDM) of functional material titanium VT6 obtained by plasma cladding with a thermal cycle. Copper is used as an electrode tool (ET) to machine functional materials. The optimum flushing flows are analyzed theoretically by using ANSYS CFX 20.1 software which is also validated by an experimental study. It was observed that while machining the functional materials to adepth of 10 mm or more, the turbulence fluid flow dominates when nozzle angles are 45° and 75°, consequently drastically affecting the quality of flushing and the performance of the EDM. For the highest machining performance, the nozzles should be at an angle of 15° relative to the tool axis. The optimum flushing at deep hole EDM process minimizes the occurrence of debris deposition on tool electrodes, thus facilitating stable machining of functional materials. The adequacy of the obtained models was confirmed experimentally. It has been established that EDM of a hole with a depth of 15 mm, an intense accumulation of sludge, is observed in the processing zone. There arebuild-ups exceeding 3 mm in cross-section after EDM. This build-up leads to a short circuit and a reduction in surface quality and productivity. It has been proven that not correct flushing leads to intensive wear of the tool and a change in its geometric shape and, accordingly, to a decrease in the quality of EDM.

Stable and Fast Electrical Discharge Machining Titanium Alloy with MIMO Adaptive Control System

In machining titanium alloy by electrical discharge machining (EDM), low heat conduction of titanium alloy often results in the temperature in the gap between electrode and work-piece rising so fast that the deionization of dielectric liquid after pulse discharging tends to be incomplete and thus induces large amount of arcing pulses in the gap burning the alloy surface. In this paper, we suggested to solve the issue of gap temperature rising fast from two aspects. First, as short ratio quantifies the extent of dielectric deionization in the gap, the electrode discharge time, which determines the amount of input energy to machine titanium alloy, should be adaptively regulated in terms of short ratio; this control action performs the function of inhibiting the heat accumulation. Second, as the heat conduction of titanium alloy is quite low, gap distance decided by servo voltage should also be tuned adaptively in terms of discharging ratio which is influenced by the heat in the gap. In this approach, two control variables were designed to form a multiple-input, multiple-output (MIMO) adaptive control system for EDM in which a two-step-ahead prediction control strategy, based on the real-time estimation of EDM process model, was employed to compute the two control variables. Confirmation experiments demonstrated that this control system in machining titanium alloy can adjust electrode discharge time and servo voltage simultaneously with respect to different machining situations to drive both the gap state, fluctuating smoothly with small amplitude around gap state expectation and the discharge ratio following discharging ratio expectation. Compared with EDM without the assisting of adaptive control systems, this MIMO EDM adaptive control system displays superior both the machining stability and the machining efficiency.

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

Fast and Stable Electrical Discharge Machining (EDM) by Two-step-ahead Predicted Control

In order to improve EDM performances, the most important issue is to develop a highly stable control system. As to a serious defect of instability in EDM machining even in EDM adaptive control systems by Minimum-variance (MV) control this paper focuses on stabilizing EDM process by extending the MV control to minimum-variance and pole-placement coupled (MVPP) control and two-step prediction (TSP) control. Based on real-time estimation of EDM process model parameters, the latter two control systems directly control electrode discharging cycle not only to follow a specified gap state for fast machining but also to track the dynamical response of a reference model for stabilizing machining processes. The experiments specially designed demonstrate that both control systems can timely adjust electrode discharging cycle in terms of different machining situations quantified as a series of varied gap states to maintain a stable and fast fabrication, while the TSP control performs even better. The adaptive control systems by the two controls exhibit superior machining ability and capability of stabilizing sparking process to the adaptive control system by MV control. The two control laws have actually theoretically and technically solved a stability issue puzzled in EDM circle for decades.

Building an EDM Process Model by an Instrumental Variable Approach

An electrical discharge machining (EDM) process model is critical in control system for stabilizing machining process and improving productivity. A two order AR model was firstly proposed for the process, but the deduced formulation of the model parameter estimates is proved biased. An instrumental variable approach is then proposed to correct the biased estimates. This approach was completed by two interactive Kalman filters. One filter provides instrumental variables, namely gap state estimates, through the latest estimated parameters; while the other uses the gap state estimates to recursively estimate a two order auto- regressive (AR) model parameters. The two Kalman filters by way of interactively supporting each other forms a new feasible way of online modeling an EDM process.

Building an EDM process model by an instrumental variable approach based on two interactive Kalman filters

An electrical discharge machining (EDM) process model is critical in control system for stabilizing EDM or micro EDM processes and improving productivity. This paper presents a new feasible method to build an online process model. A two order AR model was firstly proposed for the process, but the deduced formulation of the model parameter estimates is biased. An instrumental variable approach is then proposed to correct the biased estimates. This approach was completed by two interactive Kalman filters. One filter provides instrumental variables, gap state estimates, through the latest estimated parameters; while the other uses the gap state estimates to recursively estimate a two order auto-regressive (AR) model parameters and then provides a one-step gap state prediction. The two Kalman filters by way of interactively supporting each other forms a new feasible way of online modeling an EDM process.

Optimizing electric discharge machining parameters for tungsten-carbide utilizing thermo-mathematical modelling

The energy distribution in the Electrical Discharge Machining (EDM) process influences the material removal rate, and other machining characteristics like crater geometry, relative wear ratio and surface roughness. During this process the electrical energy is converted into heat energy and this energy is distributed among the electrode, workpiece and the dielectric fluid. The fraction of the energy which is transferred to the workpiece, is the useful energy and this energy should be maximum, for optimum utilization of energy. This fraction of energy is one of the important parameters used in the existing thermo-physical models of EDM process. Due to apparent incongruities and conflicting data early researchers conjectured the same value of fraction of energy transferred to electrodes for all machining parameters in their models for numerically calculating the volume and geometry of the crater formed. This assumption is one of the reasons of error in the models from the experimental data. So this study is planned to experimentally study the variation of this fraction of input discharge energy with the help of thermo-mathematical models during EDM of Tungsten-Carbide by varying the machining parameters current and pulse duration. The data calculated in this study can be further used in the existing thermo-physical models, expecting to bring the models preciously more close to the real conditions. This data will also be helpful for numerically calculating the optimum parameters using optimum value of the fraction of energy transferred to the electrodes especially workpiece. The results obtained showed that the energy effectively transferred to the workpiece varies with the discharge current and pulse duration from 6.5% to 17.7%, which proves that the fixed value assumed in the models is not in line with real EDM process.

Net Emission Coefficient of Plasma Excited in the Electrical Discharge Machining Through Liquid Nitrogen Dielectric Medium

In the enthalpy conservation equation of plasma model for the arc plasma columns based upon fluid description, a term appears as radiation loss which is a sink term. The purpose of this study is to calculate this radiative heat loss for electrical discharge machining (EDM) arc plasmas in the liquid nitrogen dielectric medium, using method of net emission coefficient (NEC). Assuming cylindrical, in local thermodynamic equilibrium and isothermal plasma, the NEC is calculated for two pressures of 0.1 and 1 MPa, in the working temperature of EDM plasmas range between 5500 and 10 000 K, and according to the EDM plasmas radius. Emissions of atomic lines as well as continuum radiations are considered as the origins of the NEC. The line shapes in this calculation are given by convolution of Doppler and Lorentz profiles, resulting in a simplified Voigt profile. The overlapping lines are also taken into account. Presented results for NEC can serve as input data for various numerical models of EDM process.

Adaptive control for EDM process with a self-tuning regulator

In order to improve the performance of machines, there is a growing need to develop a highly stable servo control system for electrical discharge machining (EDM). With the perpetual push towards the untended EDM operation, an adaptive control system is and will continue to be a primary option. In this paper, a new EDM adaptive control system which directly and automatically regulates tool-down-time has been developed. Based on the real-time-estimated parameters of the EDM process model, by using minimum-variance control strategy, the process controller, a self-tuning regulator, was designed to control the machining process so that the gap states follow the specified gap state. With a properly selected specified gap states, this adaptive system improves the machining rate by, approximately, 100% and in the meantime achieves a more robust and stable machining than the normal machining without adaptive control. This adaptive control system helps to gain the expected goal of an optimal machining performance.