Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Plan and Design of Experiments
2.2. Performance Measures
2.3. Grey Relational Analysis
3. Results
3.1. Statistical Significance
3.2. Effect of Near-Dry WEDM Variables on Output Measures
3.3. Grey Relational Analysis
- 1.
- Determination of S/N ratio
- 2.
- Normalization
- 3.
- Deviation
- 4.
- Grey Relational Coefficient (GRC)
- 5.
- Grey Relational Grade (GRG)
3.4. Confirmation Trial
3.5. Machined Surface Morphology
4. Conclusions
- The statistical significance of input factors through ANOVA techniques has shown that all the output measures have a significant influence on the generated regression terms. For single output measures, higher-F/lower-P numbers have identified that current was having a substantial effect on both MRR and SR, while, Toff was the largest contributor in the case of RLT.
- R2 values close to unity for all output measures have shown the suitability of the obtained results. Thus, the results obtained from the statistical findings clearly show the present study’s acceptability and fitness. The obtained results of residual plots for all performance measures implied good ANOVA results.
- The effect of near-dry WEDM variables was studied on output measures through main effect plots. It was found to have a contradictory nature of input factors to attain the desired levels of MRR, SR, and RLT.
- Grey relational analysis (GRA) has been employed to attain optimal parametric settings of multiple performance measures. GRA technique for the optimal parametric settings of simultaneous performance measures of MRR, SR, and RLT was found to be at Ton of 30 µs, Toff of 24 µs, and current of 4 A. The optimal parametric settings have resulted in an MRR value of 0.6273 mm3/sec, SR of 5.46 µm, and RLT of 6.11 µm.
- Validation trials were conducted to check the adequacy of the GRA technique. The minor acceptable deviation was recorded among the anticipated and recorded values. This clearly revealed the acceptability of the integrated approach of the Taguchi–Grey method.
- The surface morphology results obtained through SEM have shown that the near-dry WEDM process of an air-mist mixture led to a better surface quality and superior finish as compared to the wet-WEDM process of Nitinol SMA.
- The author considers that the present study will be beneficial for users working in WEDM and the near-dry WEDM process for hard machining materials.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
ANOVA | Analysis of variance |
DOE | Design of experiments |
EDM | Electrical discharge machining |
FESEM | Field emission scanning electron microscope |
GRA | Grey relational analysis |
IEG | Inter-electrode gap |
MRR | Material removal rate (mm3/sec) |
NDEDM | Near-dry electrical discharge machining |
NDWEDM | Near-dry wire electrical discharge machining |
RSM | Response surface methodology |
SEM | Scanning electron microscope |
SMA | Shape memory alloy |
SMAs | Shape memory alloys |
SME | Shape memory effect |
SR | Surface roughness (µm) |
Ton | Pulse-on-time (µs) |
Toff | Pulse-off-time (µs) |
t | Time in seconds |
RLT | Recast layer thickness |
WEDM | Wire electric discharge machine |
ρ | Density in g/cm3 |
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Parameters | Values |
---|---|
Pulse-on time (µs) | 30; 60; 90 |
Pulse-off time (µs) | 8; 16; 24 |
Current (A) | 2; 4; 6 |
Wire Electrode | Molybdenum |
Wire Diameter (mm) | 0.18 |
Sr. No. | Input Factors | Output Factors | S/N Ratios | ||||||
---|---|---|---|---|---|---|---|---|---|
Ton(µs) | Toff(µs) | Current(A) | MRR(mm3/sec) | SR(µm) | RLT(µm) | MRR | SR | RLT | |
1 | 30 | 8 | 2 | 0.3997 | 3.91 | 8.15 | −7.966 | −11.844 | −18.222 |
2 | 30 | 16 | 4 | 0.5646 | 4.87 | 6.61 | −4.965 | −13.742 | −16.407 |
3 | 30 | 24 | 6 | 0.6029 | 5.51 | 5.74 | −4.396 | −14.824 | −15.181 |
4 | 60 | 8 | 4 | 0.7114 | 5.11 | 8.12 | −2.958 | −14.168 | −18.191 |
5 | 60 | 16 | 6 | 0.7259 | 5.63 | 7.77 | −2.782 | −15.010 | −17.811 |
6 | 60 | 24 | 2 | 0.2892 | 3.80 | 6.96 | −10.777 | −11.589 | −16.852 |
7 | 90 | 8 | 6 | 0.8675 | 6.81 | 9.89 | −1.234 | −16.663 | −19.903 |
8 | 90 | 16 | 2 | 0.3744 | 4.60 | 8.73 | −8.533 | −13.263 | −18.819 |
9 | 90 | 24 | 4 | 0.5472 | 5.47 | 7.66 | −5.237 | −14.757 | −17.680 |
Source | DF | Adj. SS | Adj. MS | F-Value | p-Value |
---|---|---|---|---|---|
MRR | |||||
Regression | 3 | 0.27066 | 0.09223 | 46.65 | 0.000 |
Ton | 1 | 0.00821 | 0.00821 | 4.25 | 0.094 |
Toff | 1 | 0.04847 | 0.04847 | 25.06 | 0.004 |
Current | 1 | 0.21397 | 0.21397 | 110.63 | 0.000 |
Error | 5 | 0.00967 | 0.00193 | ||
Total | 8 | 0.28033 | |||
Standard deviation = 0.0439; R2 = 0.9655; R2 adj. = 0.9448. | |||||
SR | |||||
Regression | 3 | 6.60960 | 2.20321 | 35.50 | 0.001 |
Ton | 1 | 1.12360 | 1.12360 | 18.10 | 0.008 |
Toff | 1 | 0.18530 | 0.18530 | 2.99 | 0.145 |
Current | 1 | 5.30070 | 5.30070 | 85.41 | 0.000 |
Error | 5 | 0.31030 | 0.06206 | ||
Total | 8 | 6.91990 | |||
Standard deviation = 0.2491; R2 = 0.9552; R2 adj. = 0.9283. | |||||
RLT | |||||
Regression | 3 | 7.66330 | 2.55443 | 31.09 | 0.001 |
Ton | 1 | 3.80170 | 3.80170 | 46.27 | 0.001 |
Toff | 1 | 3.84000 | 3.84000 | 46.74 | 0.001 |
Current | 1 | 0.02160 | 0.02160 | 0.26 | 0.630 |
Error | 5 | 0.41082 | 0.08216 | ||
Total | 8 | ||||
Standard deviation = 0.2866; R2 = 0.9491; R2 adj. = 0.9186. |
Sr. No. | Normalization | Deviations | GRC | GRG | ||||||
---|---|---|---|---|---|---|---|---|---|---|
MRR | SR | RLT | MRR | SR | RLT | MRR | SR | RLT | ||
1 | 0.295 | 0.050 | 0.644 | 0.705 | 0.050 | 0.644 | 0.415 | 0.909 | 0.437 | 0.587 |
2 | 0.609 | 0.424 | 0.260 | 0.391 | 0.424 | 0.260 | 0.561 | 0.541 | 0.658 | 0.587 |
3 | 0.669 | 0.638 | 0.000 | 0.331 | 0.638 | 0.000 | 0.601 | 0.440 | 1.000 | 0.680 |
4 | 0.819 | 0.508 | 0.637 | 0.181 | 0.508 | 0.637 | 0.735 | 0.496 | 0.440 | 0.557 |
5 | 0.838 | 0.674 | 0.557 | 0.162 | 0.674 | 0.557 | 0.755 | 0.426 | 0.473 | 0.551 |
6 | 0.000 | 0.000 | 0.354 | 1.000 | 0.000 | 0.354 | 0.333 | 1.000 | 0.586 | 0.640 |
7 | 1.000 | 1.000 | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 0.333 | 0.333 | 0.556 |
8 | 0.235 | 0.330 | 0.770 | 0.765 | 0.330 | 0.770 | 0.395 | 0.602 | 0.394 | 0.464 |
9 | 0.581 | 0.624 | 0.529 | 0.419 | 0.624 | 0.529 | 0.544 | 0.445 | 0.486 | 0.491 |
Levels/Control Factors | Ton | Toff | Current |
---|---|---|---|
1 | 0.6180 | 0.5663 | 0.5634 |
2 | 0.5852 | 0.5339 | 0.5957 |
3 | 0.5034 | 0.6038 | 0.5449 |
Response Measure | Predicted Results | Confirmatory Results | % Deviation |
---|---|---|---|
MRR | 0.6142 | 0.6273 | 2.08 |
SR | 5.53 | 5.46 | 1.28 |
RLT | 5.96 | 6.11 | 2.45 |
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Vora, J.; Shah, Y.; Khanna, S.; Chaudhari, R. Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol. J. Manuf. Mater. Process. 2022, 6, 131. https://doi.org/10.3390/jmmp6060131
Vora J, Shah Y, Khanna S, Chaudhari R. Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol. Journal of Manufacturing and Materials Processing. 2022; 6(6):131. https://doi.org/10.3390/jmmp6060131
Chicago/Turabian StyleVora, Jay, Yug Shah, Sakshum Khanna, and Rakesh Chaudhari. 2022. "Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol" Journal of Manufacturing and Materials Processing 6, no. 6: 131. https://doi.org/10.3390/jmmp6060131