Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Design
2.2. Workpiece Material Fabrication
- Placing a ceramic container with aluminum in the laboratory furnace,
- Heating the laboratory furnace to 750 °C,
- Liquefying aluminum and removal of oxides from the melt surface,
- Aluminum casting into a preheated cylindrical mold at 100 °C using an electronic thermometer/heat-gun,
- Adding graphene nanostructures (GNPs) and stirring,
- Removing the split cylindrical mold after 3 min,
- Cooling in free air at room temperature of 20 °C.
2.3. CNC Turning Set-Up and Measuring Equipment
3. Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
4. Results and Discussion
4.1. Experimental Results and Major Observations
4.2. Analysis of Variance and Response Surface Regression
4.3. Implementation of NSGA-II for Multi-Objective Optimization
4.4. Confirmatory CNC Dry-Turning Experiment
5. Conclusions
- The machining process of the Al-Gr0.5% composite aluminum material introduced a highly non-linear problem that warrants further research in regard to the optimization of crucial metrics related to productivity and surface finish.
- Significant variations among the cutting parameters were observed during dry CNC turning of Al-Gr0.5% composite Al. However, it clearly appeared that the feed rate holds a dominant effect on the main cutting force and surface roughness, followed by the depth of cut and cutting speed.
- Chip formation was influenced by the content of the graphene nanoplatelets when compared to high-purity Al, thus introducing a significant change in terms of the material properties referring to both machinability criteria.
- The NSGA-II algorithm obtained beneficial non-dominated solutions for process planners to select from, with reference to the production requirements. Both machinability criteria, Fz and Ra, revealed a complex experimental search domain. This observation justifies the need for implementing intelligent algorithms for multi-criteria optimization problems related to machining processes.
- Confirmatory experimental results were in very good percentage agreement with those proposed by the NSGA-II algorithm. This encourages its application to examine and optimize the machinability aspects of a wide range of engineering materials for conventional and non-conventional material removal processes.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CCD | Central Composite Design |
RSM | Response Surface Methodology |
GNPs | Graphene Nanoplatelets |
BUE | Built-up edge |
ANOVA | Analysis of Variance |
C.I. | Confidence Interval |
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Central Composite Design of Experiments | |||||
---|---|---|---|---|---|
Parameter | Symbol | Level | |||
Low (−1) | Center (0) | High (1) | Unit | ||
Cutting speed | Vc | 60 | 90 | 120 | m/min |
Feed rate | f | 0.10 | 0.20 | 0.30 | mm/rev |
Depth of cut | α | 0.50 | 0.75 | 1.00 | mm |
Al | Si | Fe | Cu | Mg | Hf | Mn | Hg |
---|---|---|---|---|---|---|---|
96.83 | 0.18 | 0.46 | 0.22 | 1.06 | 0.05 | 1.07 | 0.00 |
CrMn | Zn | Ti | Cr | Ga | FeMn | FeSi | MgMn |
1.08 | 0.03 | 0.02 | 0.01 | 0.01 | 1.528 | 0.643 | 2.14 |
No. | Vc (m/min) | f (mm/rev) | a (mm) | Fz (N) Al | Ra (μm) Al | Fz (N) Al-Gr0.5% | Ra (μm) Al-Gr0.5% |
---|---|---|---|---|---|---|---|
1 | 60 | 0.10 | 0.50 | 46.30 | 1.80 | 37.90 | 1.20 |
2 | 60 | 0.20 | 0.75 | 159.10 | 5.80 | 141.70 | 10.40 |
3 | 60 | 0.30 | 1.00 | 300.40 | 11.80 | 287.80 | 17.00 |
4 | 90 | 0.20 | 0.50 | 128.20 | 10.60 | 114.30 | 8.80 |
5 | 90 | 0.30 | 0.75 | 263.00 | 4.60 | 254.30 | 9.60 |
6 | 90 | 0.10 | 1.00 | 89.10 | 3.00 | 87.20 | 4.00 |
7 | 120 | 0.30 | 0.50 | 181.90 | 4.00 | 174.50 | 7.80 |
8 | 120 | 0.10 | 0.75 | 75.50 | 1.00 | 81.60 | 3.40 |
9 | 120 | 0.20 | 1.00 | 232.70 | 4.20 | 228.80 | 6.00 |
Source | DF | Seq.SS | Contribution% | Adj.SS | Adj.MS | F-Val. | p-Val. |
---|---|---|---|---|---|---|---|
Model | 6 | 58,811.8 | 99.96 | 58,811.80 | 09802.00 | 0879.13 | 0.001 |
Linear | 3 | 56,181.4 | 95.49 | 44,358.30 | 14,786.10 | 1326.15 | 0.001 |
Vc (m/min) | 1 | 51.0 | 00.09 | 00369.60 | 00369.60 | 0033.15 | 0.029 |
f (mm/rev) | 1 | 43,333.0 | 73.65 | 23,643.10 | 23,643.10 | 2120.52 | 0.001 |
a (mm) | 1 | 12,797.4 | 21.75 | 04653.60 | 04653.60 | 0417.38 | 0.002 |
Two-way int. | 3 | 2630.4 | 04.47 | 02630.40 | 00876.80 | 0078.64 | 0.013 |
Vc × f | 1 | 136.2 | 00.23 | 00077.60 | 00077.60 | 0006.96 | 0.119 |
Vc × a | 1 | 2120.4 | 03.60 | 00459.40 | 00459.40 | 0041.20 | 0.023 |
f × a | 1 | 373.8 | 00.64 | 00373.80 | 00373.80 | 0033.53 | 0.029 |
Error | 2 | 22.3 | 00.04 | 00022.30 | 00011.10 | ||
Total | 8 | 58,834.1 | 100.00 | ||||
R2 | 99.96% |
Source | DF | Seq.SS | Contribution% | Adj.SS | Adj.MS | F-Val. | p-Val. |
---|---|---|---|---|---|---|---|
Model | 6 | 167.490 | 95.28 | 167.490 | 27.9149 | 06.72 | 0.013 |
Linear | 3 | 146.707 | 83.45 | 055.478 | 18.4925 | 04.45 | 0.019 |
Vc (m/min) | 1 | 021.660 | 12.32 | 012.705 | 12.7050 | 03.06 | 0.022 |
f (mm/rev) | 1 | 110.940 | 63.11 | 042.602 | 42.6021 | 10.26 | 0.085 |
a (mm) | 1 | 014.107 | 08.02 | 000.069 | 00.0688 | 00.02 | 0.091 |
Two-way int. | 3 | 020.783 | 11.82 | 020.783 | 06.9276 | 01.67 | 0.040 |
Vc × f | 1 | 018.496 | 10.52 | 008.777 | 08.7771 | 02.11 | 0.028 |
Vc × a | 1 | 001.867 | 01.06 | 000.344 | 00.3438 | 00.08 | 0.081 |
f × a | 1 | 000.420 | 00.24 | 000.420 | 00.4200 | 00.10 | 0.078 |
Error | 2 | 008.306 | 04.72 | 008.306 | 04.1530 | ||
Total | 100.00 | ||||||
R2 | 95.28% |
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Fountas, N.A.; Manolakos, D.E.; Vaxevanidis, N.M. Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning. Metals 2025, 15, 584. https://doi.org/10.3390/met15060584
Fountas NA, Manolakos DE, Vaxevanidis NM. Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning. Metals. 2025; 15(6):584. https://doi.org/10.3390/met15060584
Chicago/Turabian StyleFountas, Nikolaos A., Dimitrios E. Manolakos, and Nikolaos M. Vaxevanidis. 2025. "Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning" Metals 15, no. 6: 584. https://doi.org/10.3390/met15060584
APA StyleFountas, N. A., Manolakos, D. E., & Vaxevanidis, N. M. (2025). Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning. Metals, 15(6), 584. https://doi.org/10.3390/met15060584