A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM
Abstract
:1. Introduction
- (i)
- To find the surface roughness values using different strategies of ML approach.
- (ii)
- To find out the error percentage between the predicted value by different ML strategies and experimental values.
- (iii)
- To analyze the response variables with the machining indicators using ANOVA.
- (iv)
- To optimize the best solutions predicted by the ML approach using TLBO and perform the validation experiments at the suggested setting
- (v)
- To perform the mapping of elements for different electrodes used for machining along with the morphological analysis of the machined surface of the composite.
2. Materials and Methods
2.1. Experimental Set-Up
2.2. Response Characterization
2.3. Methodology
3. Results and Discussion
3.1. Machine Learning Perspective
3.2. Analysis of Results Evaluated from Gradient Boost Algorithm and TLBO Implementation
4. Morphological Study
5. Conclusions
- (i)
- The best ML strategy for the prediction of SR while processing Al/SiC/Gr hybrid composite on EDM is gradient boost, which exhibits an error percentage in the range of ±1% (except for six observations).
- (ii)
- The discharge current has a significant influence on SR, followed by Ton and Toff. However, tool material is hierarchically added in the quadratic model.
- (iii)
- The best SR value after the RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8 V; I: 10 A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively.
- (iv)
- At high DE level parameters, the DE is large between the tool and workpiece, and a large number of microcracks, deposited lumps, sub-surface formation, etc., are observed. However, the deposited lumps, microcracks, and uneven surfaces are significantly reduced from the machined surface, which is processed at the optimized settings suggested by RSM-ML-TLBO.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Process Parameters | Notation (Units) | Code/Level | ||
---|---|---|---|---|
−1 | 0 | 1 | ||
Pulse-on time | Ton (µs) | 30 | 60 | 90 |
Tool | - | Steel-304 | Brass | Copper |
Voltage | V (V) | 6 | 7 | 8 |
Pulse-off time | Toff (µs) | 30 | 60 | 90 |
Current | I (A) | 10 | 12 | 14 |
Run Odr | Ton (µs) | B: Toff (µs) | V (V) | I (A) | Tool | SR (µm) |
---|---|---|---|---|---|---|
1 | 60 | 30 | 7 | 12 | −1 | 3.32 |
2 | 60 | 60 | 7 | 14 | −1 | 3.49 |
3 | 60 | 60 | 6 | 12 | −1 | 3.21 |
4 | 60 | 30 | 6 | 12 | 0 | 3.39 |
5 | 60 | 60 | 7 | 12 | 0 | 2.96 |
6 | 60 | 60 | 7 | 12 | 0 | 2.84 |
7 | 60 | 60 | 7 | 12 | 0 | 2.85 |
8 | 60 | 60 | 8 | 12 | −1 | 3.32 |
9 | 90 | 90 | 7 | 12 | 0 | 3.53 |
10 | 30 | 90 | 7 | 12 | 0 | 2.92 |
11 | 60 | 30 | 8 | 12 | 0 | 3.33 |
12 | 60 | 60 | 8 | 14 | 0 | 3.16 |
13 | 60 | 60 | 8 | 10 | 0 | 2.82 |
14 | 30 | 60 | 7 | 12 | 1 | 3.31 |
15 | 60 | 30 | 7 | 14 | 0 | 3.42 |
16 | 60 | 60 | 7 | 10 | 1 | 2.88 |
17 | 60 | 90 | 7 | 12 | −1 | 3.19 |
18 | 60 | 90 | 7 | 12 | 1 | 3.25 |
19 | 90 | 60 | 7 | 10 | 0 | 3.21 |
20 | 60 | 60 | 7 | 12 | 0 | 2.93 |
21 | 90 | 60 | 7 | 12 | 1 | 3.92 |
22 | 30 | 60 | 7 | 10 | 0 | 2.54 |
23 | 90 | 60 | 7 | 14 | 0 | 3.53 |
24 | 30 | 30 | 7 | 12 | 0 | 3.21 |
25 | 60 | 90 | 7 | 14 | 0 | 2.98 |
26 | 60 | 60 | 7 | 12 | 0 | 2.89 |
27 | 60 | 30 | 7 | 10 | 0 | 2.65 |
28 | 30 | 60 | 7 | 14 | 0 | 3.28 |
29 | 60 | 60 | 8 | 12 | 1 | 3.58 |
30 | 60 | 60 | 6 | 14 | 0 | 3.17 |
31 | 90 | 30 | 7 | 12 | 0 | 3.68 |
32 | 60 | 60 | 7 | 10 | −1 | 3.25 |
33 | 90 | 60 | 8 | 12 | 0 | 3.32 |
34 | 60 | 60 | 7 | 12 | 0 | 3.3 |
35 | 60 | 60 | 6 | 12 | 1 | 3.06 |
36 | 60 | 60 | 7 | 14 | 1 | 3.67 |
37 | 90 | 60 | 6 | 12 | 0 | 3.33 |
38 | 60 | 60 | 6 | 10 | 0 | 2.51 |
39 | 90 | 60 | 7 | 12 | −1 | 3.76 |
40 | 30 | 60 | 6 | 12 | 0 | 2.69 |
41 | 60 | 30 | 7 | 12 | 1 | 3.93 |
42 | 30 | 60 | 8 | 12 | 0 | 3.22 |
43 | 60 | 90 | 6 | 12 | 0 | 3.35 |
44 | 30 | 60 | 7 | 12 | −1 | 3.36 |
45 | 60 | 90 | 8 | 12 | 0 | 2.93 |
46 | 60 | 90 | 7 | 10 | 0 | 2.69 |
Run Odr | SR (µm) | SR (µm) | SR (µm) | SR (µm) | Error | Error | Error |
---|---|---|---|---|---|---|---|
Experimental | Decision Tree | Xgboost | Random Forest | Decision Tree | Xgboost | Random Forest | |
1 | 3.32 | 3.18 | 3.32 | 3.33 | −4.192 | 0.009 | 0.269 |
2 | 3.49 | 3.31 | 3.49 | 3.31 | −5.158 | 0.002 | −5.095 |
3 | 3.21 | 3.18 | 3.21 | 3.19 | −0.909 | 0.063 | −0.773 |
4 | 3.39 | 3.18 | 3.39 | 3.24 | −6.170 | −0.056 | −4.474 |
5 | 2.96 | 3.18 | 2.96 | 3.08 | 7.461 | 0.072 | 3.983 |
6 | 2.84 | 3.18 | 2.96 | 3.08 | 12.001 | 4.300 | 8.377 |
7 | 2.85 | 3.18 | 2.96 | 3.08 | 11.608 | 3.934 | 7.997 |
8 | 3.32 | 3.18 | 3.32 | 3.26 | −4.192 | −0.014 | −1.859 |
9 | 3.53 | 3.58 | 3.53 | 3.38 | 1.457 | −0.003 | −4.360 |
10 | 2.92 | 3.18 | 2.92 | 3.08 | 8.933 | −0.006 | 5.342 |
11 | 3.33 | 3.18 | 3.33 | 3.28 | −4.479 | 0.000 | −1.649 |
12 | 3.16 | 3.31 | 3.16 | 3.21 | 4.747 | 0.046 | 1.452 |
13 | 2.82 | 2.82 | 2.82 | 2.94 | −0.044 | −0.035 | 4.281 |
14 | 3.31 | 3.18 | 3.31 | 3.32 | −3.902 | 0.049 | 0.267 |
15 | 3.42 | 3.31 | 3.42 | 3.30 | −3.216 | 0.019 | −3.459 |
16 | 2.88 | 2.82 | 2.88 | 3.07 | −2.127 | 0.018 | 6.731 |
17 | 3.19 | 3.18 | 3.19 | 3.20 | −0.287 | 0.072 | 0.178 |
18 | 3.25 | 3.18 | 3.25 | 3.30 | −2.128 | 0.007 | 1.592 |
19 | 3.21 | 2.82 | 3.21 | 3.14 | −12.188 | −0.003 | −2.080 |
20 | 2.93 | 3.18 | 2.96 | 3.08 | 8.561 | 1.097 | 5.048 |
21 | 3.92 | 3.58 | 3.92 | 3.55 | −8.637 | −0.043 | −9.455 |
22 | 2.54 | 2.82 | 2.54 | 2.85 | 10.974 | 0.030 | 12.123 |
23 | 3.53 | 3.58 | 3.53 | 3.43 | 1.457 | 0.011 | −2.943 |
24 | 3.21 | 3.18 | 3.21 | 3.24 | −0.909 | −0.001 | 1.048 |
25 | 2.98 | 3.31 | 2.98 | 3.14 | 11.074 | −0.023 | 5.435 |
26 | 2.89 | 3.18 | 2.96 | 3.08 | 10.063 | 2.496 | 6.502 |
27 | 2.65 | 2.82 | 2.65 | 2.98 | 6.368 | 0.017 | 12.428 |
28 | 3.28 | 3.31 | 3.28 | 3.19 | 0.915 | −0.042 | −2.762 |
29 | 3.58 | 3.18 | 3.58 | 3.37 | −11.150 | 0.012 | −5.817 |
30 | 3.17 | 3.31 | 3.17 | 3.14 | 4.416 | −0.009 | −0.791 |
31 | 3.68 | 3.58 | 3.68 | 3.46 | −2.679 | −0.001 | −5.998 |
32 | 3.25 | 2.82 | 3.25 | 3.04 | −13.269 | −0.143 | −6.345 |
33 | 3.32 | 3.58 | 3.32 | 3.37 | 7.874 | 0.009 | 1.387 |
34 | 3.3 | 3.18 | 2.96 | 3.08 | −3.611 | −10.239 | −6.730 |
35 | 3.06 | 3.18 | 3.06 | 3.23 | 3.949 | 0.003 | 5.651 |
36 | 3.67 | 3.31 | 3.67 | 3.40 | −9.809 | 0.018 | −7.256 |
37 | 3.33 | 3.58 | 3.33 | 3.30 | 7.550 | 0.005 | −0.790 |
38 | 2.51 | 2.82 | 2.51 | 2.85 | 12.301 | 0.090 | 13.620 |
39 | 3.76 | 3.58 | 3.76 | 3.48 | −4.749 | −0.029 | −7.431 |
40 | 2.69 | 3.18 | 2.69 | 3.04 | 18.247 | 0.137 | 12.966 |
41 | 3.93 | 3.18 | 3.93 | 3.50 | −19.063 | −0.040 | −10.904 |
42 | 3.22 | 3.18 | 3.22 | 3.14 | −1.216 | −0.080 | −2.357 |
43 | 3.35 | 3.18 | 3.35 | 3.12 | −5.050 | −0.142 | −6.740 |
44 | 3.36 | 3.18 | 3.36 | 3.23 | −5.332 | −0.009 | −3.744 |
45 | 2.93 | 3.18 | 2.93 | 3.11 | 8.561 | 0.011 | 6.236 |
46 | 2.69 | 2.82 | 2.69 | 2.89 | 4.786 | 0.050 | 7.341 |
Source | SS | df | MS | F-Value | p-Value | |
---|---|---|---|---|---|---|
Model | 3.98 | 7 | 0.57 | 17.94 | <0.0001 | significant |
A-Ton | 0.88 | 1 | 0.88 | 27.71 | <0.0001 | |
B-Toff | 0.27 | 1 | 0.27 | 8.61 | 0.0057 | |
D-I | 1.08 | 1 | 1.08 | 33.93 | <0.0001 | |
E-Tool | 0.031 | 1 | 0.031 | 0.97 | 0.332 | |
A^2 | 0.55 | 1 | 0.55 | 17.26 | 0.0002 | |
B^2 | 0.22 | 1 | 0.22 | 6.89 | 0.0124 | |
E^2 | 1.4 | 1 | 1.4 | 44.09 | <0.0001 | |
Residual | 1.21 | 38 | 0.032 | |||
Lack of Fit | 1.06 | 33 | 0.032 | 1.08 | 0.5215 | Non-significant |
Pure Error | 0.15 | 5 | 0.03 | |||
Cor Total | 5.19 | 45 |
Suggested Solutions by RSM and RSM-ML-TLBO | ||||||
---|---|---|---|---|---|---|
Technique | Ton | Toff | V * | I | Tool | SR |
RSM | 45.04 | 73.23 | 7.68 | 10 | −0.06 | 2.5865 |
RSM-ML-TLBO | 46.98 | 76.09 | - | 10 | 0.058 | 2.4696 |
Exp No. 38 | 60 | 60 | 6 | 10 | 0 | 2.51 |
Confirmation Experiments | ||||||
RSM | 45 | 73 | 8 | 10 | Brass | 2.62 |
RSM-ML-TLBO | 47 | 76 | 8 | 10 | Brass | 2.49 |
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Abbas, A.T.; Sharma, N.; Al-Bahkali, E.A.; Sharma, V.S.; Farooq, I.; Elkaseer, A. A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM. J. Manuf. Mater. Process. 2023, 7, 163. https://doi.org/10.3390/jmmp7050163
Abbas AT, Sharma N, Al-Bahkali EA, Sharma VS, Farooq I, Elkaseer A. A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM. Journal of Manufacturing and Materials Processing. 2023; 7(5):163. https://doi.org/10.3390/jmmp7050163
Chicago/Turabian StyleAbbas, Adel T., Neeraj Sharma, Essam A. Al-Bahkali, Vishal S. Sharma, Irfan Farooq, and Ahmed Elkaseer. 2023. "A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM" Journal of Manufacturing and Materials Processing 7, no. 5: 163. https://doi.org/10.3390/jmmp7050163
APA StyleAbbas, A. T., Sharma, N., Al-Bahkali, E. A., Sharma, V. S., Farooq, I., & Elkaseer, A. (2023). A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM. Journal of Manufacturing and Materials Processing, 7(5), 163. https://doi.org/10.3390/jmmp7050163