Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA
Abstract
:1. Introduction
2. Materials and Methods
2.1. Alumina Nano-Powder
2.2. Experimental Details
2.3. Optimization
3. Results and Discussion
3.1. Analysis of Nano-Powder
3.2. Experimental Results and Regression Equations
3.3. ANOVA for MRR, TWR, and SR
3.4. Normal Probability Plots
3.5. Impact of Machining Variables on Performance Measures
3.6. Optimization
3.7. Surface Morphology for Nano-Powder
4. Conclusions
- ANOVA results showed that regression model terms significantly impacted the developed model terms in the case of all the performance measures. In the case of individual variables, PC, Toff, and Ton significantly impacted the output measures of MRR, TWR, and SR, respectively. Verifying the normal probability plot yielded good ANOVA results and satisfied the necessary condition for ANOVA. Thus, all the statistical findings revealed the acceptability and fitness of the suggested models;
- The main effect plots were observed to have a positive impact on the addition of PC on all the performance measures. The addition of PC enhanced MRR, TWR, and SR;
- The TLBO algorithm has shown optimal parametric settings of current at 24 A, PC at 4 g/L, Toff at 10 µs, and Ton of 4 µs. Moreover, it has shown optimal input parameters of 43.57 mg/min for MRR, 6.478 mg/min for TWR, and 3.73 µm for SR. Pareto points with unique solutions were generated by considering the need of users for specific applications;
- Lastly, scanning electron microscopy (SEM) was used for the machined surface analysis. The obtained SEM graph established that using an Al2O3 nano-powder concentration of 4 g/L improved the quality of the machined parts by decreasing surface defects.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
ANOVA | Analysis of variance |
DF | Degree of freedom |
DOE | Design of Experiments |
EDM | Electrical discharge machining |
IEG | Inter-electrode gap |
MRR | Material removal rate (mg/min) |
NPMEDM | Nano-powder-mixed electrical discharge machining |
PC | Powder concentration |
PMEDM | Powder-mixed electrical discharge machining |
SEM | Scanning electron microscope |
SMA | Shape memory alloy |
SMAs | Shape memory alloys |
SR | Surface roughness (µm) |
TEM | Transmission electron microscope |
TLBO | Teaching–learning based optimization |
TWR | Tool wear rate (mg/min) |
Ton | Pulse on time (µs) |
Toff | Pulse off time (µs) |
T | Time in minutes |
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Parameters | Values |
---|---|
Current (A) | 8; 16; 24 |
Pulse-off time (µs) | 4; 7; 10 |
PC (g/L) | 0, 2, 4 |
Pulse-on time (µs) | 4; 7; 10 |
Size of nano-powder (nm) | 100–150 |
Spark gap (mm) | 0.01 |
Cutting depth (mm) | 2 |
Tool | Copper |
Trial | Current (A) | Toff (µs) | PC (g/L) | Ton (µs) | MRR (mg/min) | TWR (mg/min) | SR (µm) |
---|---|---|---|---|---|---|---|
1 | 8 | 4 | 0 | 4 | 22.87 ± 0.17 | 7.23 ± 0.17 | 4.27 ± 0.17 |
2 | 8 | 7 | 2 | 7 | 31.21 ± 0.31 | 6.98 ± 0.11 | 4.11 ± 0.11 |
3 | 8 | 10 | 4 | 10 | 36.71 ± 0.28 | 6.63 ± 0.22 | 3.89 ± 0.16 |
4 | 16 | 4 | 2 | 10 | 43.53 ± 0.22 | 8.13 ± 0.24 | 5.12 ± 0.22 |
5 | 16 | 7 | 4 | 4 | 47.04 ± 0.25 | 6.64 ± 0.13 | 3.92 ± 0.12 |
6 | 16 | 10 | 0 | 7 | 19.23 ± 0.11 | 7.05 ± 0.17 | 4.17 ± 0.13 |
7 | 24 | 4 | 4 | 7 | 53.18 ± 0.26 | 7.50 ± 0.19 | 4.41 ± 0.16 |
8 | 24 | 7 | 0 | 10 | 24.79 ± 0.18 | 8.01 ± 0.14 | 5.01 ± 0.19 |
9 | 24 | 10 | 2 | 4 | 31.98 ± 0.19 | 6.83 ± 0.16 | 4.03 ± 0.15 |
Source | DF | SS | MS | F-Value | p-Value |
---|---|---|---|---|---|
MRR | |||||
Regression | 4 | 1047.44 | 261.86 | 41.82 | 0.002 |
Current | 1 | 61.24 | 61.24 | 9.78 | 0.035 |
Toff | 1 | 167.15 | 167.15 | 26.69 | 0.007 |
PC | 1 | 817.39 | 817.39 | 130.53 | 0.000 |
Ton | 1 | 1.65 | 1.65 | 0.26 | 0.635 |
Error | 4 | 25.05 | 6.26 | ||
Total | 8 | 1072.49 | |||
TWR | |||||
Regression | 4 | 2.4037 | 0.6009 | 41.11 | 0.002 |
Current | 1 | 0.3742 | 0.3742 | 25.60 | 0.007 |
Toff | 1 | 0.9249 | 0.9249 | 63.27 | 0.001 |
PC | 1 | 0.3894 | 0.3894 | 26.64 | 0.007 |
Ton | 1 | 0.7151 | 0.7151 | 48.92 | 0.002 |
Error | 4 | 0.0584 | 0.0146 | ||
Total | 8 | 2.4622 | |||
SR | |||||
Regression | 4 | 1.5116 | 0.3778 | 13.68 | 0.013 |
Current | 1 | 0.2321 | 0.2321 | 8.40 | 0.044 |
Toff | 1 | 0.4873 | 0.4873 | 17.65 | 0.014 |
PC | 1 | 0.2521 | 0.2521 | 9.13 | 0.039 |
Ton | 1 | 0.5400 | 0.5400 | 19.56 | 0.011 |
Error | 5 | 0.1105 | 0.0276 | ||
Total | 8 | 1.6220 |
Condition/Response | MRR (mg/min) | TWR (mg/min) | SR (µm) |
---|---|---|---|
Predicted from TLBO | 43.57 | 6.478 | 3.73 |
Experimental values | 44.13 | 6.331 | 3.61 |
% Error | 1.28 | 2.26 | 3.21 |
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Chaudhari, R.; Shah, Y.; Khanna, S.; Patel, V.K.; Vora, J.; Pimenov, D.Y.; Giasin, K. Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA. Materials 2022, 15, 7392. https://doi.org/10.3390/ma15207392
Chaudhari R, Shah Y, Khanna S, Patel VK, Vora J, Pimenov DY, Giasin K. Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA. Materials. 2022; 15(20):7392. https://doi.org/10.3390/ma15207392
Chicago/Turabian StyleChaudhari, Rakesh, Yug Shah, Sakshum Khanna, Vivek K. Patel, Jay Vora, Danil Yurievich Pimenov, and Khaled Giasin. 2022. "Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA" Materials 15, no. 20: 7392. https://doi.org/10.3390/ma15207392