Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor
Abstract
:1. Introduction
2. Materials and Methods
- Infill Percentage—The infill percentage measures the amount of material inside the fabricated part. It represents the density of the part. The infill percentage is set according to the part’s requirement. The values for the infill percentage are chosen as 10, 33, 55, 78, and 100%.
- Layer Thickness—In FDM, the thickness of a single layer deposited by the nozzle is the layer thickness. The type of nozzle used decides the paper thickness. The nozzle diameter used in this case is 0.4 mm. The values for the layer height are chosen as 0.08 mm, 0.16 mm, 0.24 mm, 0.32 mm, and 0.4 mm.
- Print Speed—The speed of the nozzle with which the material will be deposited is denoted as the print speed. A very high printing speed will cause the wear and tear of physical parts and lead to improper distribution of materials. Moreover, a low printing speed is not suitable as it will increase the time required to print one specimen. Hence, the values for print speed are set as 20, 35, 50, 65, and 80 mm/s.
- Extrusion Temperature—The extrusion temperature is the temperature at which the material is extruded from the nozzle. The extruder contains a heater that heats the material up to a semi-liquid state. As the temperature increases, the viscosity of the material increases. As a result, it is necessary to set the extruder temperature within the limits where semi-liquid materials can be kept. The capacity of the heating system decides the temperatures at which materials are extruded. Temperatures are set at 190, 200, 210, 220, and 230 °C.
3. Results and Discussion
3.1. Analysis Using Desirability Approach
3.2. Analysis by Machine Learning Using a Nonlinear Regressor
3.3. Comparison of Results
4. Conclusions
- It was found that the infill percentage has a maximum effect on tensile strength and flexural strength, while extrusion temperature has a maximum effect on impact strength.
- Mathematical models for tensile strength, impact strength, and flexural strength were developed using nonlinear regression.
- Eventually, optimum values of tensile strength, impact strength, and flexural strength were found using the desirability approach and nonlinear regression and were validated experimentally.
- The desirability approach predicts the tensile strength, impact strength, and flexural strength with a percentage error of less than 3.109, 6.532, and 3.712, respectively.
- The nonlinear regression approach predicts the tensile strength, impact strength, and flexural strength with a percentage error of less than 2.977, 6.532, and 3.474, respectively.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference No. | Input Process Parameters | Filament Material | Method/Technique | Output Parameters |
---|---|---|---|---|
[18] | Infill density, infill pattern, print speed, and print temperature | PLA, ABS, CFR-PLA, CFR-ABS, CNT-ABS | One-variable-at-a-time | Tensile, compressive, flexural |
[19] | Infill density and angle of orientation | PLA | Full factorial | Tensile |
[20] | Raster angle | PLA | One-variable-at-a-time | Tensile, fracture |
[21] | Layer thickness, raster width, airgap, and part orientation | PLA, ABS | Response surface methodology | Geometrical deformation, surface roughness |
[22] | Raster angle, raster width, and layer height | PLA | Adaptive neuro-fuzzy interface system | Tensile |
[23] | Infill density, speed, and print temperature | PLA | Central compoiste design, genetic algorithm, adaptive neuro-fuzzy interface system, artifical neural network | Tensile |
[24] | Infill density, print speed, and layer thickness | PLA | Taguchi method | Tensile |
[25] | Infill density, layer thickness, and extrusion temperature | PLA | Taguchi method | Tensile, impact, and hardness |
[26] | Layer height, shell thickness, infill density, orientation angle, and print speed | PLA | Taguchi method | Tensile |
[27] | Layer thickness, airgap, orientation, temperature | PLA | One-variable-at-a-time, chemical treatment | Tensile |
[28] | Infill density | PLA | Full factorial | Tensile, hardness, impact, flexural |
[29] | Build direction, infill percentage, and layer thickness | CFR-PLA | The technique for order of preference by similarity to ideal solution | Tensile, izod impact |
[30] | Layer thickness, nozzle temperature, bed temperature, infill density | PLA | One-variable-at-a-time | Tensile |
[31] | Infill density, print speed, and layer height | CFR-PLA | Taguchi | Tensile |
[32] | Print orientation, bed temperature, nozzle temperature, print speed, infill density | CFR-PLA | Taguchi | Tensile, impact |
[33] | Infill density and print pattern | PLA | Taguchi | Tensile |
[34] | Infill density and infill pattern | PLA, ABS, PETG | Full factorial | Tensile |
[35] | Print speed and print temperature | PLA | Full factorial | Tensile |
[36] | Print orientation and layer thickness | PLA | Full factorial | Tensile |
[37] | Infill density number of aluminum layer and bed temperature | PLA | Taguchi method | Tensile |
[38] | Layer height, infill percentage, and infill pattern | PLA | Central composite design | Tensile |
[39] | Layer thickness, print orientation | PLA | Generalized-relative root-mean-square error | Tensile |
[40] | Layer height, fill density, printing velocity, and orientation | PLA | Taguchi method | Tensile |
[41] | Raster angle and moisture content | PLA | Design of experiments | Tensile, strain, modulus of elasticity |
[42] | Layer height, extrusion width, printing temperature, printing speed | FR-PLA | Central composite design | Tensile |
[43] | Bed temperature, extrusion temperature | PLA, CF-PLA | Central composite design | Tensile, flexural, shear |
Run Nos. | Infill Percentage | Layer Height (mm) | Print Speed (mm/s) | Extrusion Temp (°C) | Tensile Strength (N/mm2) | Impact Strength (kJ/m2) | Flexural Strength (N/mm2) |
---|---|---|---|---|---|---|---|
1 | 78 | 0.32 | 35 | 220 | 46.17 | 1.55 | 39.07 |
2 | 10.5 | 0.24 | 50 | 210 | 42.78 | 1.59 | 51.01 |
3 | 33 | 0.16 | 35 | 220 | 45.87 | 3.2 | 43.13 |
4 | 33 | 0.32 | 35 | 200 | 41.18 | 3.32 | 30.9 |
5 | 33 | 0.16 | 65 | 200 | 43.59 | 3.31 | 37.8 |
6 | 100.5 | 0.24 | 50 | 210 | 54.20 | 3.37 | 62.51 |
7 | 78 | 0.16 | 35 | 200 | 51.88 | 3.31 | 53.06 |
8 | 33 | 0.32 | 65 | 200 | 43.19 | 3.25 | 44.74 |
9 | 78 | 0.32 | 65 | 200 | 50.34 | 3.31 | 48.2 |
10 | 33 | 0.16 | 65 | 220 | 45.72 | 3.27 | 42.79 |
11 | 78 | 0.16 | 35 | 220 | 53.35 | 3.35 | 52.27 |
12 | 55.5 | 0.24 | 50 | 210 | 49.67 | 3.22 | 53.93 |
13 | 33 | 0.32 | 35 | 220 | 45.08 | 3.3 | 42.88 |
14 | 55.5 | 0.24 | 50 | 190 | 47.56 | 3.37 | 48.38 |
15 | 55.5 | 0.24 | 50 | 210 | 48.39 | 3.38 | 51.15 |
16 | 78 | 0.32 | 65 | 220 | 46.49 | 3.2 | 56.59 |
17 | 55.5 | 0.24 | 50 | 210 | 47.21 | 3.38 | 50.53 |
18 | 55.5 | 0.24 | 50 | 210 | 48.30 | 3.36 | 62.67 |
19 | 55.5 | 0.24 | 50 | 230 | 50.15 | 1.71 | 52.21 |
20 | 33 | 0.32 | 65 | 220 | 43.35 | 3.32 | 40.58 |
21 | 55.5 | 0.24 | 50 | 210 | 45.33 | 3.47 | 53 |
22 | 55.5 | 0.24 | 80 | 210 | 45.56 | 3.38 | 52.18 |
23 | 78 | 0.16 | 65 | 200 | 49.84 | 3.35 | 50.73 |
24 | 55.5 | 0.24 | 20 | 210 | 48.51 | 3.52 | 39.54 |
25 | 55.5 | 0.08 | 50 | 210 | 42.63 | 3.37 | 44.21 |
26 | 55.5 | 0.4 | 50 | 210 | 42.87 | 3.17 | 45.64 |
27 | 55.5 | 0.24 | 50 | 210 | 47.14 | 3.47 | 46.05 |
28 | 78 | 0.32 | 35 | 200 | 45.17 | 3.41 | 60.88 |
29 | 55.5 | 0.24 | 50 | 210 | 47.07 | 3.43 | 55.67 |
30 | 78 | 0.16 | 65 | 220 | 50.99 | 3.2 | 57.67 |
31 | 33 | 0.16 | 35 | 200 | 43.75 | 3.35 | 42.55 |
Responses (Output Parameter) | Desirability Approach | Nonlinear Regression | Experimental | Error (%) Desirability Approach | Error (%) Nonlinear Regression |
---|---|---|---|---|---|
Tensile Strength (N/mm2) | 59.6875 | 59.6069 | 57.832 | 3.109 | 2.977 |
Impact Strength (kJ/m2) | 4.3020 | 4.3020 | 4.021 | 6.532 | 6.532 |
Flexural Strength (N/mm2) | 65.3043 | 65.1432 | 62.88 | 3.712 | 3.474 |
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Jatti, V.S.; Sapre, M.S.; Jatti, A.V.; Khedkar, N.K.; Jatti, V.S. Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor. Appl. Syst. Innov. 2022, 5, 112. https://doi.org/10.3390/asi5060112
Jatti VS, Sapre MS, Jatti AV, Khedkar NK, Jatti VS. Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor. Applied System Innovation. 2022; 5(6):112. https://doi.org/10.3390/asi5060112
Chicago/Turabian StyleJatti, Vijaykumar S., Mandar S. Sapre, Ashwini V. Jatti, Nitin K. Khedkar, and Vinaykumar S. Jatti. 2022. "Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor" Applied System Innovation 5, no. 6: 112. https://doi.org/10.3390/asi5060112
APA StyleJatti, V. S., Sapre, M. S., Jatti, A. V., Khedkar, N. K., & Jatti, V. S. (2022). Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor. Applied System Innovation, 5(6), 112. https://doi.org/10.3390/asi5060112