1 Introduction
2 Literature review
2.1 The application of robotics in electrical discharge machining
2.2 Benefits and pitfalls of robotic Milling EDM
3 The design of the 6-axis robotic milling EDM system
3.1 Adopted 6-axis robot
3.2 Milling EDM end-effector
3.3 Simulating robot vibration in Milling EDM
Component description | Material | Young’s modulus | Poisson’s ratio | Bulk modulus | Tensile strength | Tensile yield strength |
---|---|---|---|---|---|---|
(MPa) | [1] | (MPa) | (MPa) | (MPa) | ||
End-effector housing | Plastic, ABS | 1628 | 0.4089 | 2978.4 | 36.26 | 27.44 |
Robot structure | Cast iron | 1.22e + 05 | 0.275 | 90,370 | 426 | 234 |
Robot gearboxes | Structural Steel | 2e + 05 | 0.3 | 1.6667e + 05 | 460 | 250 |
Spindle | ||||||
Bolts and nuts | ||||||
Ball bearings | ||||||
Spindle chuck and stator | Aluminium Alloy | 0.71 e + 05 | 0.33 | 69,608 | 310 | 280 |
Pulleys | ||||||
Timing belt | Polyisoprene, hard rubber | 397* longitudinal direction | 0.499 | 15,000 | 25 | 47 |
MEDM electrode tube | Brass | 1.12 e + 05 | 0.31 | 140,000 | 338 | 133.3 |
Mode | Natural frequency (Hz) | Total deformation range (mm) | |
---|---|---|---|
Minimal | Maximal | ||
1. | 54.301 | 1.3684e − 017 | 29.485 |
2. | 60.484 | 1.0069e − 017 | 22.988 |
3. | 85.046 | 2.8108e − 017 | 28.98 |
4. | 108.63 | 1.6191e − 017 | 40.514 |
5. | 148.82 | 1.017e − 017 | 64.114 |
6. | 166.85 | 1.5228e − 018 | 72.821 (highest) |
7. | 180.01 | 4.0202e − 017 | 45.075 |
8. | 385.02 | 2.556e − 016 | 21.402 |
9. | 398.46 | 5.3439e − 016 | 16.059 |
10. | 528.42 | 1.3407e − 015 | 15.205 (lowest) |
3.4 Robotic milling EDM control
3.4.1 Adaptive fuzzy EDM controller
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A normal pulse is the desired and useful pulse. In it, gap voltage drops from maximal value only after the programmed pulse-off time, while the current rises proportionally until the pulse is once more turned off by the programmed pulse-off time.
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Arcing occurs when several pulses hit the same area resulting in continuous material removal. It is caused by inductive reactance in one ionisation point, preventing the dielectric fluid from deionising. It is a harmful effect since it creates larger craters, increasing surface roughness. Nonetheless, it is frequently unavoidable yet should be kept low as possible as a trade-off with the intended SR vs MRR.
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A short circuit occurs when the wire electrode touches the workpiece by (1) direct contact or (2) indirectly by debris accumulation. A short circuit is identified when the current curve is found at maximum while the voltage is at minimum. Also, a short pulse does not remove material.On the other hand, disturbances in pulse frequency are controlled by modulating pulse-off time. The main causes of frequency peaks are as follows:
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Robot speed running faster them MRR, increasing short pulses.
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Flushing is inefficient, causing debris accumulation and lower dielectric resistivity, resulting in unstable pulse-off time between cycles.
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Insufficient pulse-off time causes arcing. Since a series of involuntary pulses characterise arcing, frequency peaks will occur.
where GE1, GE2, and GCE2 are scale factors respectively found, in this case, as 0.5, 1, and 10. The inputs are fuzzified using five linguistic variables of negative big (NB), negative small (NS), zero (ZO), positive small (PS), and positive big (PB).abnormal pulses = short-circuit pulses + arcing pulsese1 = (reference frequency - sparking frequency) × GE1e2 = (reference abnormal pulse ratio - current abnormal ratio) × GE2ce2 = (current abnormal pulse ratio error - previous abnormal pulse ratio error) × GCE2
where GE1, GE2, and GCE2 are scale factors respectively found, in this case, as 0.5, 1, and 10. The inputs are fuzzified using five linguistic variables of negative big (NB), negative small (NS), zero (ZO), positive small (PS), and positive big (PB).Robot speed (%) = Δu1 × \(^{1}\big/_{100}\)Pulse-off time (μs) = [ (pulse-off time set) \(\times {\mathrm{\Delta u}}_{2}\times ^{20}\big/_{100}\)] + (pulse-off time set).
3.4.2 Offline cutting path programming and wear compensation
3.4.3 Discharge gap calibration
Parameter | Value |
---|---|
Electrode | Brass Ø7 × Ø2 mm |
Workpiece | Tool steel P20–DIN 1.2738 |
Dielectric fluid | Deionised water |
Dielectric temperature (°C) | 25 |
Dielectric pressure (bar) | 0.2 |
Spindle rotation (RPM) | 1800 |
Cycling time of the fuzzy controller | 10 ms |
Voltage (V) | 120 |
Current (A) | 14 |
Pulse on time (μs) | 18 |
Pulse off time (μs) | 24 |
Pulse frequency | ~ 23 kHz |
Pulse mode | Iso-frequency |
Polarity | Electrode (+) Workpiece (−) |
Maximal robot speed (mm/min) | 15 |
4 Experimental results and discussion
4.1 Robotic MEDM adaptive fuzzy controller
4.2 Vibration assessment of robotic Milling EDM
4.3 Robotic MEDM machining characterisation
4.4 Morphological surface analysis
4.5 Electrode wear and surface morphology
Cutting path | Machining surface (M) | Side surface (C) | Corner radius (S) |
---|---|---|---|
(1) Corner cut 45° | 6.62 | 0.47 | 0.197 |
(2) Full pocket 90° | 2.52 | 1.66 | 0.293 |
(3) Half pocket 90° | 6.01 | 0.54 | 0.215 |
4.6 Three-dimensional metrology analysis
Cutting path | Measurement length (ML) | Bottom error (Be) | Corner error (Ce) | Unbalance error (Ue) | Electrode diameter (UØ) | Practical diameter (PØ) |
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(1) Corner cut 45° | 50 | 0.001 | 0.001 | 0.008 | 7.0 | 7.016 |
(2) Full pocket 90° | 50 | 0.007 | 0.007 | 0.095 | 7.0 | 7.19 |
(3) Half pocket 90° | 50 | 0.008 | 0.008 | 0.112 | 7.0 | 7.22 |
5 Conclusions
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A fuzzy logic strategy has been developed and implemented successfully for adaptive control of robotic electric discharge machining substantiated with experimental results. This outcome represents a new manufacturing system that can machine hard-to-cut workpieces in complex shapes within the large and complex capabilities of industrial robots working envelopes.
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Extremely challenging vibration and robot pose are solved problems making robotic milling EDM an excellent technique to overcome robotic chatter in machining hard-to-cut materials. The experimental results show that the EDM surface morphology and accuracy do not deteriorate. Nonetheless, shorter electrodes and the appropriate rotational balance calibration may improve the remaining minor vibrations and machining accuracy.
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The research shows that the EDM process does not generate forces or vibrations beyond the low stiffness of the robot, even with the presence of dielectric pressure to support the EDM process. In other words, adopting a flowing dielectric from the centre of the electrode through a reduced gap distance of 20 μm creates forces that can push the electrode away from the workpiece, creating disturbances or even interrupting the discharge. Furthermore, the ability of complex robot movements can be explored to preferably approach the machined area so that the gravitational field helps to flush debris and improve MRR and SR.
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Offline programming, including compensation, is proved effective. Since this manufacturing process is new, no CAM software has the post-processor to generate the necessary robot control code suitable for this research. Thus, it reveals a research opportunity to develop CAM software to generate complex milling EDM cutting paths with embedded compensation as an opportunity for future research.
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Lastly, even though current industrial robots are designed for fast speed movements of thousands of millimetres per second, it was found feasible to operate at EDM speeds as slow as 0.5 mm per minute. Also, the robot time response limitation of up to 0.3 s delay to implement a commanded movement was found acceptable for EDM purposes once predictive aspects of frequency monitoring and pulse off time adaptive online modulation are in place.