An Adaptive Speed Control System for Micro Electro Discharge Machining
Yeo, S.H., Aligiri, E., Tan, P.C. andZarepour, H.
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
Abstract The integration of the state-of-the-art monitoring and adaptive control technologies can substantially improve the performance of EDM process. This paper reports the development of an adaptive speed control system for micro EDM which demands a higher level of accuracy.
Monitoring of the machining state is conducted during the machining process so that the conditions are analysed continuously. Various schemes for the machining state are used for decision making. For instance, upon recognition of abnormal discharges, the developed adaptive speed control system would adjust the electrode feeding speed in an attempt to correct the machining state. Experimental verification shows that the proposed system can improve the machining time by more than 50%. In addition, a more accurate machined feature can be produced as compared to traditional EDM servo control systems.
Keywords: Micro EDM, Online monitoring. Pulse discriminating system. Adaptive speed control system.
PACS: 52.80s
INTRODUCTION
Since its introduction to the manufacturing industry in 1943, Electrical Discharge Machining (EDM) has become one of the best techniques to machine high strength and electrically conductive materials into complex shapes. The fact that EDM is now the fourth most popular machining process, with more equipment for EDM being sold than those for all other processes except milling, turning and grinding [1], shows that it has become the industries' most utilised non-conventional machining process. This may be attributed partly to the integration of control system such as computer numerical control (CNC) into the process which allows operators to exploit the full capability of EDM technique. However, although most commercial EDM machines incorporate some kind of process control, highly skilled operators are still needed to select and adjust the optimal machining setting. In EDM, the optimal working point changes during the process and any drift from it may cause instability to the process. It then becomes essential to have an adaptive control in EDM as this process involves random changes in the working condition, a large number of process parameters and high frequency of pulses. It provides a means of erosion rate optimization while maintaining surface roughness, surface damage and tool wear within a certain limit.
The self-adaptive control system is defined as one that provides a means of continuously measuring the system's performance in relation to a given criterion of
CPl 181, Third Manufacturing Engineering Society International Conference edited by V. J. Segui and M. J. Reig
figure of merit, and a means of automatically modifying the system's adjustable parameters by closed-loop actions so that the figure of merit may be satisfied [2]. In EDM processes, this definition implies that adaptive control system should provide a function of measuring the dynamics of the machining state, detecting the trend towards unstable machining and giving a proper reaction to adapt itself by the change of the working environment. Adaptive control becomes easily feasible to be implemented in EDM as a large number of control variables are electronic signals.
Consequently, research works are conducted in this field to develop a reliable adaptive control in EDM so as to improve the machining performance.
The requirement of accuracy is more demanding in micro EDM since minute imperfections are magnified in the micro machining world. Therefore incorporating an effective adaptive control strategy will enhance the micro EDM capabilities. However, very limited research work has been reported in development of adaptive control for micro EDM. This research attempts to develop an adaptive control system in micro EDM, which is divided into two main parts: development of online pulse monitoring and development of control strategy to improve the machining performances.
EDM ADAPTIVE CONTROL
EDM is a process whereby conductive materials are eroded by means of successive electrical sparks. The electrical sparks are closely influenced by the conditions at the tool-workpiece gap. The most common control system in traditional EDM is to measure the average voltage across the tool-workpiece gap. Subsequently results are compared with a predetermined servo reference voltage value to provide the means of feeding the electrode toward or retracting it from the workpiece. However, the selection of reference voltage is still highly depended on operator's experience. The detecting speed of harmful process is also still within human response range.
Kruth et al. developed an adaptive control system that automatically searches for machine settings coinciding with optimal working condition [3]. Average gap voltage is still utilized as the detecting parameter but the servo reference voltage is optimized online, corresponding to the machining status. In addition, dielectric flushing rates and pulse off-time, which have substantial influence for de-ionizing the gap is also optimized during machining process. Some researchers believe that average gap voltage is not a good indicator of the stochastic and dynamic nature of the machining process inside the EDM gap [4] and instead studied voltage characteristics in pulse regime where pulse are generally categorized as open circuit voltage, normal discharge, arcing and short circuit. It is generally accepted that each gap pulse contains information regarding the amount of material eroded at workpiece [5] and tool [6]. An EDM pulse discriminator has been successfully developed by Dauw et al. [7] and used for time history measurements to provide a detailed pulse analysis useful in monitoring removal rate and tool wear. Snoeys and Cornellissen utilized ignition delay time within each pulse to distinguish normal spark and harmful arc [8]. Pulses following an ignition delay were considered as normal spark, whereas sparks without ignition delay were categorized as harmful arc. Week and Konig [9] also utilized the same spark distinguishing criteria to develop adaptive control which contained feed optimization and pulse interval control. Furthermore ignition delay time and fall time
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were used by Week as parameters to develop an adaptive control for EDM die sinking [10]. The developed controller used separate gain for feeding and retracting and optimized the duration of drive pulses. In other investigations, it has been experimentally proven that in many machining conditions the harmful arcs also take place with ignition delay time [11]. Hence a model reference adaptive control EDM, which adjusted the discharge gap in accordance with the desired time ratios of each gap states has been proposed in [4]. Again servo reference voltage becomes the parameter being adjusted. Different monitoring approach has been proposed in [2, 12]
where radio frequency (R.F) signal is utilized instead of gap voltage or current waveform. It is reported that R.F signal drops as the discharge diverts from sparking to arcing.
Recent advancements in computing technology has brought fuzzy logic rule as a tool for optimizing the EDM parameters. EDM fuzzy logic servo control system was developed by Boccadoro et al. [13] for die sinking process. The developed system was able to optimize the spark parameters (namely, off-time, on-time, current, open voltage, flushing and servo reference voltage) as a function of the active surface and flushing conditions in order to maximize the machining speed by employing fuzzy logic rules. The use of unfixed fuzzy rules, known as a self-adjusting fuzzy control, to improve the machining performance has been investigated [14]. The adaptive control using fuzzy logic control strategy has also been implemented in WEDM [15-17].
Other types of adaptive control system in EDM die sinking have been reported as a self tuning adaptive auto jumping control system [18, 19]
ONLINE PULSE MONITORING FOR MICRO EDM PROCESS
Online monitoring system is needed to detect the onset of harmful discharges. In this work an online pulse discriminating system (PD) system has been developed to classify the gap pulses and perform an early recognition of any process instability.
Although a number of pulse classification techniques have been described above, none of them is applicable for micro EDM, since it generally uses RC type pulse generator which has different waveform characteristics as compared to conventional EDM waveform. Furthermore, micro EDM minimizes discharge energy which results in very short pulse duration (in sub-micro second regime); thus data acquisition system with a high sampling rate must be employed for monitoring in micro EDM.
Pulse Observation
Figure 1 shows the typical profile of micro EDM discharge waveforms using a RC type based pulse generator. In this work the pulses are categorized into four types, namely normal discharge, delayed discharge, arcing, and short circuit. These pulses are observed to have a different profile from that of transistor-based pulses. A normal discharge is the productive discharge pulse which normally contributes mostly for material removal. This pulse is randomly distributed along the eroding surface indicating that the degree of contamination in the gap is low. Voltage drops rapidly while current is flowing. For normal discharges, the voltage may drop below zero because the high discharge energy leading to the rush of massive electrons to anode
instantly and cause a short-term swap of anode and cathode [20] . It is noticed from Figure 1 that the normal discharge has a high peak current value compared to other types of discharges. Ideally, the RC type pulse generator should also produce pulses with constant discharge duration as discharges take place when the capacitor is fully charged. However in practice, discharge occasionally occurs when capacitor is not fully charged, thereby resulting in a pulse shape similar to normal discharge but with a lower current amplitude and discharge duration. This type of pulse is categorised as a delayed discharge as it is an analogy of a pulse with long ignition delay time in a transistor based EDM that generates a pulse train voltage. This type of pulse is reported to have an insignificant effect on material removal and result in arcs, shorts, and instability of the operation [9]. Arc is considered as a harmful discharge since the voltage has not returned to open circuit voltage when the successive discharge occurs, indicating that the dielectric has not fully deionised. Arcing will cause repetitive impact of spark on the same physical location which damages the work-material and tool. The term 'arcing' is granted since it has essentially the same destructive effect on the workpiece as a long duration arc, even though the current waveform comprises discrete pulses [21]. In micro EDM, arc discharge always comes in groups with very short interval and low current amplitude value. Short circuit pulse occurs when there is a metal to metal connection between tool electrode and workpiece. It can be notified when the current is flowing while the voltage remains zero. This type of pulse will result in no material removal.
Normal Discharge Delayed Discharge Arcing Short Circuit
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FIGURE 1. Micro EDM waveform and its classification
Pulse Classification Strategy
From the description of the classified pulses above, a discriminating strategy is proposed in this research work based on two main parameters: peak current and number of pulse captured in a predetermined time. The use of current pulse gives better representation of discharge energy rather than voltage pulse since it is widely believed that the material removal process in micro EDM happens due to a highly localized heat caused by a massive electron movement in the plasma channel [22]. The pulse measurement duration is set to be equal to a summation of pulse-on time and pulse off-time of one normal discharge pulse in a certain machining setting. This
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implies that at most, there will be only one normal discharge or delayed discharge captured in one acquisition, while for arcing several pulses can be captured in one acquisition as it comes in a group of pulses with a short interval. In addition to the main parameters, a peak current dividing value is also introduced as a secondary discrimination parameter. Delayed discharge is observed to have a peak current lesser than 0.8 maximum peak current as it lacks sufficient charged voltage. This current dividing value is empirically determined through a number of pulse observations from various machining settings and is found to be fairly comparable with voltage dividing values reported by Kwon et al. [23]. After the pulse is discriminated, it will be stored to count the number of each pulse type detected during machining. The flowchart of the pulse classification strategy is given in figure 2.
FIGURE 2. Flowchart of pulse discrimination strategy
DEVELOPMENT OF ADAPTIVE SPEED CONTROL SYSTEM FOR MICRO EDM
In order to build a rigid construction for detecting the trend towards harmful arcing, pulse distribution analyses are conducted with the developed desktop micro EDM.
Desktop M i c r o E D M Setup
The experimental rig used in this work is given in Figure 3. Discharge waveforms at the tool-workpiece gap are captured using a differential probe and a current probe.
These signals are acquired by a PCI based digitizers NI-5112 which has a capability to obtain 100 milhon samples per second. Each acquisition is characterized by 1000 points in order to capture the real shape of discharge waveforms and the acquisition length is fixed to capture one normal discharge cycle (pulse on time + pulse off time).
A real time pulse monitoring and discriminating program is developed using visual
programming software, LabVIEW and the interface is shown in Figure 4. An inconspicuous variation in the percentage of each pulse type, pulse on-time, and percentage of the machining progress can be monitored in real time. The feedback signal is sent to ESP 300 motion controller to control the X-Y-Z position of the electrode. This motion controller has an accuracy of O.Ol^im and communication rate of 10 kHz (or 9600 baud rate).
PD System Intorfaco
DC Power
Supply
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Generator Pulso Voltage probeCurrent probe
CCTV Camera FIGURE 3. Photograph of the developed micro EDM setup
FIGURE 4. Interface of the real time pulse monitoring program
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Pulse Distribution Analysis
The developed pulse monitoring and discriminating program was used for actual machining with the desktop micro EDM. The machining condition of the experiment work is given in Table 1. Figure 5 shows the proportion of each pulse type at various machining depth settings under a constant feeding speed of 0.5nm/s. It is observed that the pulse train is mainly composed of normal discharge pulses, while delayed discharge, arcing and short circuit play minor roles throughout the machining process.
High proportion of normal discharge pulse means that the process possesses a high stability and may be attributed to a low feeding speed which gives more time for debris to be flushed away. As the machining depth is increased, the percentage of normal discharge is found to decrease while the percentages of other pulse types increased non-linearly. When the machining depth progressed further, a higher aspect ratio hole is created and debris removal becomes more difficult. The un-removed metal debris in the gap makes the dielectric become more conductive and initiates the occurrence of secondary discharges before the capacitor is fully charged or more severely before the dielectric is fully deionised, thus giving rise in the occurrence of delayed discharge and arcing, respectively.
TABLE 1. Machining condition for experiment Workpiece Material
Electrode Material Electrode Diameter Dielectric Fluid Open Circuit Voltage Capacitor Setting Polarity
AISI 4140 alloy steel Tungsten Rod
300 ^m
Daphne Cut HL-25 synthetic electric spark oil 100 V
4000 pF
Workpiece positive, Electrode negative
Figure 6 shows pulse type distribution under various electrode feeding speed for drilling 900|^m holes depth. As the feeding speed increases, the percentage of normal discharge drops while the percentage of delayed discharge, arcing and short circuit increases. The trend is attributed to feeding speed; as feeding speed increases, the time to flush away the debris becomes lesser. Hence, the flushing quality deteriorates and percentage of secondary discharges is raised.
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FIGURE 5. Pulse distribution under various machining setting
95
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te
0.2 0,5 1Feeding Speed {|jm/s)
I Norma Discharge
B Delayed Discharge
0,2 0.5 1 Feeding Speed {|im/s) FIGURE 6. Pulse distribution under various feeding speed setting
Observation during machining reveals a high probability of short circuit pulse onset after an episode of arc pulses that occur after delayed discharge pulses. Thus this sequence can be used as a triggering criteria to retract the electrode before more harmful condition arises. When short circuit or arc pulses occur, the average gap voltage will drop and the control strategy will command the electrode to retract so as to restore the flushing condition. More retraction movement will result in longer machining time. However, applying slower feeding speed has been found to be able to help restoring the machining stability as indicated by a higher normal discharge percentage. These phenomena are the underlying principles for the development of an adaptive speed control system for micro EDM. Experiments result implies that during the machining process, there is an optimum feeding speed to avoid excessive retracting movement which give rise to machining time. The adaptive control system will adjust the feed rate to find the optimum feeding speed which varies throughout the machining process.
Adaptive Speed Control System Performance
The basic elements of the developed adaptive speed control system are the pulse discriminating system and pulse distribution analyzer. Gap voltage and current signal captured during the process, are discriminated by the developed PD system, each pulse is counted and the distribution of each pulse is analyzed. Results of pulse distribution analysis perform two main functions: determining the electrode movement direction, feeding or retracting, and modifying the feed rate of the electrode. Retraction of electrode from the workpiece takes place when the following conditions occur:
• Short circuit pulse is detected
• Arc pulses are detected after delayed discharge pulses
The pulse type distribution is used to determine the feed rate gain which will reduce the initial feeding speed input. The higher the percentage of arc pulses, delayed discharge pulses, or short circuit pulses acquired, the higher the feed rate gain will be given and this reduces more feeding speed. Figure 7 shows the flowchart of the developed adaptive speed control system. Actual machining is carried out and a comparison of the proposed strategy with the traditional servo control system is shown in Figure 8.
V 4f
PD System
f Number / A of Normal 1
T Discharge 1 V recorded V f Number f A of Delay [ T Discharge i V recorded V
/ Number / N of Arcing |
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Pulse Distribution
Analysis
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Initial feeding spe input
k
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EDM Process
F I G U R E 7. Flowchart of the developed adaptive speed control system
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Machining Depth (^m) (b)
•Adaptivespeed servo system Common Servo teed system
F I G U R E 8. Performance of adaptive speed control system with feeding speeds: (a) 0.5|rm/s, (b) 1 |rm/s
The plots in Figure 8 show the machining time versus machining depth for drilling depths of 900|^m. The feeding speeds used are 0.5|^m/s and 1 ^im/s respectively. These experiments were conducted with the similar machining conditions shown in Table 2.
For the initial feeding speed of 0.5|^m/s, the developed system was able to improve the machining speed up to 38 minutes or 57.5 % while for initial feeding speed of 1 |^m/s the improvement was 32 minutes or 54.95% with respect to the machining time of traditional EDM servo system. This improvement is attributed to PD system and adaptive speed feature. PD system differentiates discharge pulses and gives a corrective action for each acquisition; thus the quality and response speed of the control system are enhanced. The adaptive speed feature automatically adjusts the
feeding speed to provide better flushing condition and reduces the number of retracting movement, which affects the overall machining time. Figure 9 also shows that during the early stage of machining, the adaptive speed control system did not improve the machining time. The effect of adaptive speed was progressively observed as the depth increased.
Figure 9 shows the resulting feature of the micro holes. The depth of the resulting features is found to be considerably far from the desired depth of 900|^m. This difference is attributed to electrode longitudinal wear and error of determining zero position for both control systems. However, it can be observed that the adaptive speed control system produce more accurate depth as compared to that deployed in a common servo feed system. The depth error is reduced to 2.9% for initial feeding speed of 0.5 ^im/s and 4% for initial feeding speed of 1 ^im/s. Since the machining time for adaptive speed system is shorter than the common servo feed system, it gives rise to lesser electrode longitudinal wear and thus the resulting in better feature accuracy.
735 lam 709 \ivn 737jim 701 urn
( a ) 0 , 5 n m / s (b) 1 (im/s
efiii*--.!
Figure 9. Result of machined features
CONCLUSIONS
A new adaptive speed control system for micro EDM has been developed. It utilizes gap current pulse to monitor and discriminate discharge pulse into normal discharge, delayed discharged, arcing, and short circuit in real time. The real time pulse monitoring system was integrated with a desktop micro EDM machine. Online pulse monitoring observations indicated a high probability of short circuit pulse onset after an episode of arc pulses that occur after delayed discharge pulses. Furthermore, applying slower electrode feed rate has been proven to improve machining stability, indicated by an increased proportion of normal discharge pulses. A control strategy has been developed based on machining characteristics associated with a RC based pulse generator. The developed adaptive speed control system was able to improve the machining speed by twice that of the traditional servo speed control system. More accurate dimensional features were also produced with reduction of dimensional error by up to 4%. The developed online pulse monitoring system has been shown to give a remarkable advantage when it is deployed as an integral part in micro EDM control system. It has a high potential to produce accurate micro features in a short machining
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time. Future studies will use the developed online pulse monitoring system on an online tool wear compensation system for micro EDM.
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