Optimization of Friction Stir Welding Parameters in Hybrid Additive Manufacturing: Weldability of 3D-Printed Poly(methyl methacrylate) Plates
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Setup
2.2. Design of Experiments
2.3. Evaluation of the Experimental Process
3. Results and Discussion
3.1. FSW Process Experiments and Evaluation
3.2. Statistical Modeling and Optimization of the Results of the Mechanical Tests
- The Pin Profile B (PPB) tool optimizes all metrics (E, sB, and WT). PPB has a conical shape, in contrast with PPA, which is cylindrical. Consequently, the contact area between the tool and the 3D-printed material is more extensive, resulting in higher energy generation due to friction, and a higher welding temperature. In addition, better mixing and homogenization are achieved, leading to a better mechanical response in the welded area (E and sB).
- The rotational speed increases the welding temperature, modulus E, and ultimate tensile strength in the welded area. In addition, the increase in the RS increases the heat produced in the contact area between the plasticized material and the pin area, and better mixes the mass between the tool’s leading and trailing edges. Therefore, the increase in RS increases the WT, E, and sB in the particular experimental space.
- The increase in TS also increases the weld temperature and the weld mechanical response. The transverse speed increases the tangential force, which interacts with the friction coefficient between the pin surface and the plasticized material, producing higher heat transfer rates, resulting in higher WT, E, and sB.
- Figure 8b–d show the interaction plots between tool, Ra, and sB versus E, sB, and WT. These plots can decompose the interaction type—linear or nonlinear—between the processing parameters. It can be observed that the trend lines are smooth in all three cases for all interactions between the tool, RS, and TS, showing linear interactions with cross-products (i.e., synergistic interactions) (Table 2).
- There is no similar work in the literature with which to correlate and compare the experimental results of this study. Tensile test results, when joining bulk PMMA sheets with FSW, are in good agreement with the results of this study [20]. In the former work, the ultimate tensile strength was found to be higher than the results of the present study, which is to be expected, as 3D-printed samples have inferior strength to the corresponding bulk (solid) samples.
4. Conclusions
- According to the MEP diagrams and ANOVA, transverse speed and rotational speed are the most influential parameters, with very high F-values (F > 60, p = 0.000).
- Tool geometry is not significant for tensile modulus of elasticity (E) (F = 2.09 < 4 and p = 0.157 > 0.05) in the specific experimental area, but is very substantial for the ultimate tensile strength (sB) and the welding temperature (WT) (F > 18 and p < 0.05).
- The processing parameters mainly affect the tangential force and the resulting mixing quality of the welded material. PPB tool geometry, 1400 rpm rotational speed, and 9 mm/min transverse speed maximized the process welding temperature, ultimate tensile strength, and tensile modulus of elasticity.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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3D Printing Parameters | Value | Units |
---|---|---|
Printing Orientation | ±45 | ° |
Layer Thickness | 0.20 | mm |
Bed Temperature | 105 | °C |
Nozzle Temperature | 240 | °C |
Number of Parameters | 2 | - |
Top Solid Layers | 7 | - |
Bottom Solid Layers | 4 | - |
Fill Density | 100 | % |
Travel Speed | 40 | mm/s |
FSW Parameters | Value | Units |
Rotation Speed | 600–1000–1400 | rpm |
Welding Speed | 3–6–9 | mm/min |
Pin Profile | PPA–PPB | - |
Shoulder Diameter | 12 | mm |
Tool Tilt Angle | 0 | ° |
Stand-off Distance | 0 | mm |
Tool Inclination Angle | 90 (vertical) | ° |
Tool Material | AISI 304 |
No | PP (A = 1; B = 2) | RS (rpm) | TS (mm/min) | WT (°C) | sB (MPa) | E (MPa) | sB/sB* | E/E* |
---|---|---|---|---|---|---|---|---|
1 | 1 | 600 | 3 | 59.4 | 7.13 | 187.11 | 25.9% | 63.3% |
2 | 1 | 600 | 3 | 63.4 | 8.09 | 206.39 | 29.4% | 69.9% |
3 | 1 | 600 | 3 | 53.2 | 6.54 | 191.92 | 23.8% | 65.0% |
4 | 1 | 600 | 6 | 73.0 | 10.21 | 270.58 | 37.1% | 91.6% |
5 | 1 | 600 | 6 | 73.1 | 12.17 | 304.09 | 44.2% | 102.9% |
6 | 1 | 600 | 6 | 70.7 | 13.56 | 296.36 | 49.3% | 100.3% |
7 | 1 | 600 | 9 | 79.0 | 10.05 | 259.67 | 36.5% | 87.9% |
8 | 1 | 600 | 9 | 83.0 | 11.33 | 307.11 | 41.2% | 103.9% |
9 | 1 | 600 | 9 | 80.9 | 14.22 | 268.58 | 51.7% | 90.9% |
10 | 1 | 1000 | 3 | 33.1 | 1.69 | 39.64 | 6.1% | 13.4% |
11 | 1 | 1000 | 3 | 36.7 | 2.15 | 43.05 | 7.8% | 14.6% |
12 | 1 | 1000 | 3 | 35.3 | 2.60 | 45.92 | 9.5% | 15.5% |
13 | 1 | 1000 | 6 | 97.1 | 13.66 | 260.78 | 49.6% | 88.3% |
14 | 1 | 1000 | 6 | 91.5 | 13.94 | 268.74 | 50.6% | 91.0% |
15 | 1 | 1000 | 6 | 87.9 | 12.51 | 238.99 | 45.5% | 80.9% |
16 | 1 | 1000 | 9 | 78.1 | 16.21 | 316.39 | 58.9% | 107.1% |
17 | 1 | 1000 | 9 | 80.3 | 13.44 | 322.38 | 48.8% | 109.1% |
18 | 1 | 1000 | 9 | 83.7 | 17.08 | 286.35 | 62.1% | 96.9% |
19 | 1 | 1400 | 3 | 73.2 | 18.93 | 289.48 | 68.8% | 98.0% |
20 | 1 | 1400 | 3 | 86.2 | 22.76 | 305.63 | 82.7% | 103.4% |
21 | 1 | 1400 | 3 | 77.1 | 21.16 | 290.16 | 76.9% | 98.2% |
22 | 1 | 1400 | 6 | 88.6 | 5.04 | 129.39 | 18.3% | 43.8% |
23 | 1 | 1400 | 6 | 94.0 | 5.97 | 142.90 | 21.7% | 48.4% |
24 | 1 | 1400 | 6 | 93.4 | 6.74 | 181.95 | 24.5% | 61.6% |
25 | 1 | 1400 | 9 | 146.0 | 27.84 | 391.02 | 101.1% | 132.3% |
26 | 1 | 1400 | 9 | 136.6 | 22.23 | 397.77 | 80.8% | 134.6% |
27 | 1 | 1400 | 9 | 143.8 | 26.42 | 347.88 | 96.0% | 117.7% |
28 | 2 | 600 | 3 | 63.8 | 10.17 | 246.42 | 36.9% | 83.4% |
29 | 2 | 600 | 3 | 59.2 | 12.49 | 294.68 | 45.4% | 99.7% |
30 | 2 | 600 | 3 | 61.3 | 8.82 | 220.81 | 32.1% | 74.7% |
31 | 2 | 600 | 6 | 67.0 | 1.62 | 39.02 | 5.9% | 13.2% |
32 | 2 | 600 | 6 | 65.3 | 1.30 | 34.25 | 4.7% | 11.6% |
33 | 2 | 600 | 6 | 69.4 | 1.27 | 35.42 | 4.6% | 12.0% |
34 | 2 | 600 | 9 | 92.2 | 6.88 | 222.80 | 25.0% | 75.4% |
35 | 2 | 600 | 9 | 87.3 | 6.53 | 191.98 | 23.7% | 65.0% |
36 | 2 | 600 | 9 | 97.1 | 7.16 | 186.19 | 26.0% | 63.0% |
37 | 2 | 1000 | 3 | 76.8 | 7.21 | 207.47 | 26.2% | 70.2% |
38 | 2 | 1000 | 3 | 74.6 | 7.34 | 224.85 | 26.7% | 76.1% |
39 | 2 | 1000 | 3 | 82.1 | 8.39 | 238.29 | 30.5% | 80.6% |
40 | 2 | 1000 | 6 | 97.4 | 19.63 | 289.37 | 71.3% | 97.9% |
41 | 2 | 1000 | 6 | 99.6 | 20.90 | 311.85 | 75.9% | 105.5% |
42 | 2 | 1000 | 6 | 104.7 | 19.71 | 282.91 | 71.6% | 95.7% |
43 | 2 | 1000 | 9 | 112.7 | 27.90 | 366.79 | 101.4% | 124.1% |
44 | 2 | 1000 | 9 | 117.7 | 28.59 | 377.90 | 103.9% | 127.9% |
45 | 2 | 1000 | 9 | 109.2 | 27.19 | 347.67 | 98.8% | 117.7% |
46 | 2 | 1400 | 3 | 88.6 | 5.04 | 129.39 | 18.3% | 43.8% |
47 | 2 | 1400 | 3 | 94.0 | 5.97 | 142.90 | 21.7% | 48.4% |
48 | 2 | 1400 | 3 | 93.4 | 6.74 | 181.95 | 24.5% | 61.6% |
49 | 2 | 1400 | 6 | 146.0 | 27.84 | 391.02 | 101.1% | 132.3% |
50 | 2 | 1400 | 6 | 136.6 | 22.23 | 397.77 | 80.8% | 134.6% |
51 | 2 | 1400 | 6 | 143.8 | 26.42 | 347.88 | 96.0% | 117.7% |
52 | 2 | 1400 | 9 | 119.2 | 24.23 | 374.40 | 88.0% | 126.7% |
53 | 2 | 1400 | 9 | 126.7 | 26.38 | 355.55 | 95.8% | 120.3% |
54 | 2 | 1400 | 9 | 118.7 | 23.51 | 364.66 | 85.4% | 123.4% |
Min | 33.1 | 1.27 | 34.25 | 4.6% | 11.6% | |||
Max | 146.0 | 28.59 | 397.77 | 103.9% | 134.6% | |||
Average | 88.9 | 13.61 | 248.04 | 49.5% | 83.9% |
WT Versus Tool; RS; TS | |||||
Source | DoF | SoS | MS | F | p |
Tool | 1 | 3054.0 | 3054.02 | 199.06 | 0.000 |
RS | 2 | 14,782.1 | 7391.04 | 481.76 | 0.000 |
TS | 2 | 13,678.3 | 6839.14 | 445.78 | 0.000 |
Tool × RS | 2 | 1400.7 | 700.33 | 45.65 | 0.000 |
Tool × TS | 2 | 369.5 | 184.74 | 12.04 | 0.000 |
RS × TS | 4 | 1706.4 | 426.61 | 27.81 | 0.000 |
Tool × RS × TS | 4 | 4519.2 | 1129.79 | 73.64 | 0.000 |
Error | 36 | 552.3 | 15.34 | ||
Total | 53 | 40,062.5 | |||
S = 3.91687; R-sq = 98.62%; R-sq(adj) = 97.97%; R-sq(pred) = 96.90%; | |||||
sB versus Tool; RS; TS | |||||
Source | DoF | SoS | MS | F | p |
Tool | 1 | 42.29 | 42.289 | 18.22 | 0.000 |
RS | 2 | 878.46 | 439.231 | 189.26 | 0.000 |
TS | 2 | 849.77 | 424.885 | 183.07 | 0.000 |
Tool × RS | 2 | 341.81 | 170.907 | 73.64 | 0.000 |
Tool × TS | 2 | 122.00 | 60.999 | 26.28 | 0.000 |
RS × TS | 4 | 531.05 | 132.764 | 57.21 | 0.000 |
Tool × RS × TS | 4 | 970.68 | 242.669 | 104.56 | 0.000 |
Error | 36 | 83.55 | 2.321 | ||
Total | 53 | 3819.61 | |||
S = 1.52343; R-sq = 97.81%; R-sq(adj) = 96.78%; R-sq(pred) = 95.08% | |||||
E versus Tool; RS; TS | |||||
Source | DoF | SoS | MS | F | p |
Tool | 1 | 848 | 848.0 | 2.09 | 0.157 |
RS | 2 | 54,315 | 27,157.6 | 67.01 | 0.000 |
TS | 2 | 139,189 | 69,594.5 | 171.71 | 0.000 |
Tool × RS | 2 | 76,764 | 38,382.2 | 94.70 | 0.000 |
Tool × TS | 2 | 4476 | 2238.2 | 5.52 | 0.008 |
RS × TS | 4 | 81,130 | 20,282.5 | 50.04 | 0.000 |
Tool × RS × TS | 4 | 193,849 | 48,462.3 | 119.57 | 0.000 |
Error | 36 | 14,591 | 405.3 | ||
Total | 53 | 565,163 | |||
S = 20.1321; R-sq = 97.42%; R-sq(adj) = 96.20%; R-sq(pred) = 94.19% |
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Vidakis, N.; Petousis, M.; Mountakis, N.; Kechagias, J.D. Optimization of Friction Stir Welding Parameters in Hybrid Additive Manufacturing: Weldability of 3D-Printed Poly(methyl methacrylate) Plates. J. Manuf. Mater. Process. 2022, 6, 77. https://doi.org/10.3390/jmmp6040077
Vidakis N, Petousis M, Mountakis N, Kechagias JD. Optimization of Friction Stir Welding Parameters in Hybrid Additive Manufacturing: Weldability of 3D-Printed Poly(methyl methacrylate) Plates. Journal of Manufacturing and Materials Processing. 2022; 6(4):77. https://doi.org/10.3390/jmmp6040077
Chicago/Turabian StyleVidakis, Nectarios, Markos Petousis, Nikolaos Mountakis, and John D. Kechagias. 2022. "Optimization of Friction Stir Welding Parameters in Hybrid Additive Manufacturing: Weldability of 3D-Printed Poly(methyl methacrylate) Plates" Journal of Manufacturing and Materials Processing 6, no. 4: 77. https://doi.org/10.3390/jmmp6040077