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Abas, M.;  Habib, T.;  Noor, S.;  Salah, B.;  Zimon, D. Fused Deposition Modeling of 3D Printed Part . Encyclopedia. Available online: https://encyclopedia.pub/entry/27548 (accessed on 18 April 2024).
Abas M,  Habib T,  Noor S,  Salah B,  Zimon D. Fused Deposition Modeling of 3D Printed Part . Encyclopedia. Available at: https://encyclopedia.pub/entry/27548. Accessed April 18, 2024.
Abas, Muhammad, Tufail Habib, Sahar Noor, Bashir Salah, Dominik Zimon. "Fused Deposition Modeling of 3D Printed Part " Encyclopedia, https://encyclopedia.pub/entry/27548 (accessed April 18, 2024).
Abas, M.,  Habib, T.,  Noor, S.,  Salah, B., & Zimon, D. (2022, September 24). Fused Deposition Modeling of 3D Printed Part . In Encyclopedia. https://encyclopedia.pub/entry/27548
Abas, Muhammad, et al. "Fused Deposition Modeling of 3D Printed Part ." Encyclopedia. Web. 24 September, 2022.
Fused Deposition Modeling of 3D Printed Part 
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This entry is adapted from the peer-reviewed paper 10.3390/polym14173667

Fused deposition modeling (FDM) is the most economical additive manufacturing (AM) technology available for fabricating complex part geometries. Dimensional instabilities are challenges of FDM that depends on printing parameters. The selected printing parameters include layer height, number of perimeters, infill density, infill angle, print speed, nozzle temperature, bed temperature, and print orientation. Three-level definitive screening design (DSD) was used to plan experimental runs. 

fused deposition modeling polylactic acid (PLA) dimensional deviation definitive screening design

1. Introduction

Among the AM technologies, fused deposition modeling (FDM) is one of the most widely used additive manufacturing technologies because of its economy and ability to process a diverse range of materials, including polymers and metals [1]. However, the use of FDM printing for part fabrication is still a challenge because of the involvement of numerous process parameters and because the choice of materials affects the part quality, mechanical strength, and development time [2][3]. Depending on the application, careful consideration of process variables and material selection is necessary. According to the published reports, the process parameters can be divided into three major sets [4]. The first set of parameters includes the process-related parameters, such as infill speed, number of shells, thickness of shells, bed temperature, fill density, layer height, nozzle temperature, print speed, air gap, and raster angle. The second set of parameters includes the machine-specific parameters, such as nozzle diameter, filament width, bed adhesion type, and filament diameter. The third set of parameters is related to part geometry, such as the part’s orientation and special features.
To achieve good dimensional accuracy in FDM-printed parts, the optimal process parameter settings are crucial, as they vary according to material, complexity of part geometry, material type, and chemical composition [5][6]. Therefore, finding the optimal settings and combination of parameters can be challenging and laborious. Additionally, most of the polymers used in FDM are semi-crystalline and prone to part distortion due to crystallization [7]. Therefore, the process requires trial and error experimental procedures, or application of the design of experiments (DoE), to achieve excellent quality prints with desirable mechanical properties. The most common semi-crystalline polymers are polylactic acid (PLA), polypropylene (PP), polycaprolactone (PCL), polyethylene (PE), and polybutylene terephthalate (PBL). Moreover, the dimensional specifications may vary for the same material as well as for varied materials. For instance, in PLA, positive deviation (expansion) is observed in the width and thickness direction, while negative deviation (shrinkage) is observed in the length direction [8].
PLA is considered a green material because it is made through the polymerization of lactic acid by the fermentation of renewable resources. There are four different forms of crystals, namely α, β, and γ [9]. The α crystals show two disordered modifications i.e., α′ and α′′ [9]. The α crystal is obtained through cold, melt, or solution crystallization at a higher temperature (i.e., above 120 °C) [10], while α′ is produced at a lower temperature (i.e., below 100 °C) by mixing α and α′ between 100 °C and 120 °C [11]. The α′′ crystal is obtained through crystallization at a temperature (0 °C to 30 °C) under high-pressurized CO2 [12]. The α′ crystal forms the chain conformation of the PLA chain, which is more disordered than in the α form crystal [13]. Therefore, the α form provides lower elongation at break, higher Young’s modulus, and better preservation against water vapor than the α′ form. The α′′ crystal produces poor chain packing and the lowest crystal density compared to α and α′ [14]. The published studies have shown that the α form crystal is more stable compared to its other forms [9]. The β form crystal is obtained through α crystal deformation and through annealing or stretching at elevated temperatures [15]. The γ form is obtained by epitaxial growth on a hexamethyl benzene substrate [16].
The physical and mechanical properties of PLA are influenced by the degree of crystallinity. Mechanical properties can be improved by thermal annealing to increase the degree of crystallinity [17]. In FDM printing, the degree of crystallinity in the bottom layers is higher than in the top and side layers because of the bed temperature, which causes the layer to cool down slowly, thus rendering the printed part dimensionally unstable [18].

2. Fused Deposition Modeling of 3D Printed Parts

Numerous studies have been reported that investigated the effect of FDM process parameters on quality characteristics, mechanical properties, physical properties, energy consumption, and build time for diverse types of materials. For instance, Galetto et al. [4] investigated the effect of process parameters on the process efficiency and quality of PLA printed parts. Quadratic models were developed for surface roughness and dimensional accuracies. For maximizing dimensional accuracy, the design features of parts play a significant role. Kitsakis et al. [19] studied the dimensional accuracy of FDM-printed parts for medical applications. In the study, they considered different parameters, including the material type (PLA and ABS), layer height, infill rate, and the number of shells, as well as studying the dimensional accuracy. The study revealed that the best dimensional accuracy for PLA material was attained at an infill rate of 50%, with one shell, and a layer height of 0.3 mm. The study of Aslani et al. [20] showed that the extrusion temperature significantly affects the dimensional accuracy and surface roughness of PLA printed parts. The study proved that by applying grey relational analysis, high extrusion temperature (230 °C) combined with medium wall thickness values (2 mm) optimized both surface roughness and dimensional accuracy. Nathaphan and Trutassanawin [21] concluded that for good dimensional accuracy and compression strength, the layer height and print speed must be set at a low level, the nozzle temperature at a high level, while the bed temperature must be above the glass transition temperature of ABS material. Further, shrinkage occurs in the diameter of the cylinder because of the cooling and solidification of molten polymer. However, expansion was noticed in height of the cylinder due to the rounding of the number of layers to the higher integer number. Basavaraj and Vishwas [22] found that layer thickness affects the tensile strength, manufacturing time, layer thickness, shell thickness, and orientation angle. Further, the study concluded that tensile strength and dimensional accuracy decrease with an increase of the layer thickness and increase with increases of the orientation angle and shell thickness. The study of Lalegani Dezaki et al. [23] revealed that surface quality and mechanical properties are directly affected by the type of patterns. Concentric and grid patterns exhibit good surface quality and tensile strength while the zigzag pattern produces the worst surface roughness and mechanical properties. Padhi et al. [24] noted that shrinkage occurs along the width and length directions, while the thickness increases in parts printed from acrylonitrile-butadiene-styrene (ABSP 400). The shrinkage may develop inner stress upon solidification. Further, the formation of inner layer cracks and weak interlayer adhesion decrease the dimensional accuracy of final parts. Vahabli and Rahmati [25] improved the surface quality of FDM-printed parts for medical devices using artificial neural networks based on the feed-forward back propagation (FFBP) algorithm. Parts were printed from ABSplus material. The successful fabrication of medical devices such as a molar tooth, femur, skull, and stem further confirms the performance of FFBP. Deswal et al. [8] worked on FDA process parameters by applying an approach integrated with a response surface methodology, artificial neural network-genetic algorithm (ANN-GA), genetic algorithm (RSM–GA), and artificial neural network (ANN) for improving the dimensional accuracy of ABS parts. The adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm (WOA) was applied by Sai et al. [26] to optimize the process parameters for printing PLA implants. Their study concluded that layer thickness followed by raster angle and infill density significantly affects the surface roughness, while layer thickness and raster angle at low level and infill density at medium level provides good surface quality. The findings of Vyavahare et al. [27] revealed that layer thickness and build orientation have a significant effect on fabrication time and surface roughness, while for dimensional accuracy, in addition to these two parameters, Camposeco-Negrete [28] optimized the process parameters to improve the dimensional accuracy, energy consumption, and the production time of FDM 3D printed acrylonitrile styrene acrylate (ASA) parts. The study showed that printing plane is the most significant parameter that helps in reducing production time and energy consumption. For dimensional accuracy, the infill pattern influences the width of the part, and layer thickness affects the length of the part significantly. Mohamed et al. [29] applied a deep neural network to analyze and optimize the dimensional accuracy of FDM PC-ABS printed parts. In the study, a total of 16 experiments were planned based on a definitive screening design (DSD). The part profile for dimensional accuracy was considered as the percentage variation in diameter and length. The quadratic model was found to be significant for both length and diameter variation. Slice thickness, print direction, interaction of print direction, and deposition angle were found to be significant for length variation. Mohanty et al. [30] applied the hybrid approach of a Taguchi- MACROS- nature-inspired heuristic optimization technique to optimize parameters affecting the dimensional precision of ABS M30 FDM-printed parts. Their results showed that part orientation significantly affected dimensional precision. All of the nature-inspired algorithms considered in the study provide comparable results for minimizing dimensional error. Garg et al. [31] studied the dimensional accuracy and surface roughness of ABS P430 FDM-printed parts under the cold vapor technique using acetone. The results revealed that chemical treatment reduces surface roughness and improves the dimensional accuracy of the final part. This may be attributed to softening of the external layer, because acetone causes rupturing of a secondary bond between the chains of ABD polymers and reaches a more stable position.

References

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  2. Popescu, D.; Zapciu, A.; Amza, C.; Baciu, F.; Marinescu, R. FDM Process Parameters Influence over the Mechanical Properties of Polymer Specimens: A Review. Polym. Test. 2018, 69, 157–166.
  3. Liu, Z.; Wang, Y.; Wu, B.; Cui, C.; Guo, Y.; Yan, C. A Critical Review of Fused Deposition Modeling 3D Printing Technology in Manufacturing Polylactic Acid Parts. Int. J. Adv. Manuf. Technol. 2019, 102, 2877–2889.
  4. Galetto, M.; Verna, E.; Genta, G. Effect of Process Parameters on Parts Quality and Process Efficiency of Fused Deposition Modeling. Comput. Ind. Eng. 2021, 156, 107238.
  5. Azdast, T.; Hasanzadeh, R. Polylactide Scaffold Fabrication Using a Novel Combination Technique of Fused Deposition Modeling and Batch Foaming: Dimensional Accuracy and Structural Properties. Int. J. Adv. Manuf. Technol. 2021, 114, 1309–1321.
  6. Rajan, K.; Samykano, M.; Kadirgama, K.; Harun, W.S.W.; Rahman, M.M. Fused Deposition Modeling: Process, Materials, Parameters, Properties, and Applications. Int. J. Adv. Manuf. Technol. 2022, 120, 1531–1570.
  7. Samy, A.A.; Golbang, A.; Archer, E.; McIlhagger, A.T. A Comparative Study on the 3D Printing Process of Semi-Crystalline and Amorphous Polymers Using Simulation. In Proceedings of the UKACM 2021 Conference, Loughborough University, Online, 14–16 April 2021; Loughborough University: Loughborough, UK, 2021.
  8. Deswal, S.; Narang, R.; Chhabra, D. Modeling and Parametric Optimization of FDM 3D Printing Process Using Hybrid Techniques for Enhancing Dimensional Preciseness. Int. J. Interact. Des. Manuf. 2019, 13, 1197–1214.
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  10. Wasanasuk, K.; Tashiro, K. Crystal Structure and Disorder in Poly (l-Lactic Acid) δ Form (A’ Form) and the Phase Transition Mechanism to the Ordered α Form. Polymer 2011, 52, 6097–6109.
  11. Di Lorenzo, M.L.; Rubino, P.; Immirzi, B.; Luijkx, R.; Hélou, M.; Androsch, R. Influence of Chain Structure on Crystal Polymorphism of Poly (Lactic Acid). Part 2. Effect of Molecular Mass on the Crystal Growth Rate and Semicrystalline Morphology. Colloid Polym. Sci. 2015, 293, 2459–2467.
  12. Marubayashi, H.; Akaishi, S.; Akasaka, S.; Asai, S.; Sumita, M. Crystalline Structure and Morphology of Poly (L-Lactide) Formed under High-Pressure CO2. Macromolecules 2008, 41, 9192–9203.
  13. Pan, P.; Zhu, B.; Kai, W.; Dong, T.; Inoue, Y. Effect of Crystallization Temperature on Crystal Modifications and Crystallization Kinetics of Poly (L-lactide). J. Appl. Polym. Sci. 2008, 107, 54–62.
  14. Cocca, M.; Di Lorenzo, M.L.; Malinconico, M.; Frezza, V. Influence of Crystal Polymorphism on Mechanical and Barrier Properties of Poly (l-Lactic Acid). Eur. Polym. J. 2011, 47, 1073–1080.
  15. Echeverría, C.; Limón, I.; Muñoz-Bonilla, A.; Fernández-García, M.; López, D. Development of Highly Crystalline Polylactic Acid with β-Crystalline Phase from the Induced Alignment of Electrospun Fibers. Polymers 2021, 13, 2860.
  16. Brizzolara, D.; Cantow, H.-J.; Diederichs, K.; Keller, E.; Domb, A.J. Mechanism of the Stereocomplex Formation between Enantiomeric Poly (Lactide) S. Macromolecules 1996, 29, 191–197.
  17. Wach, R.A.; Wolszczak, P.; Adamus-Wlodarczyk, A. Enhancement of Mechanical Properties of FDM-PLA Parts via Thermal Annealing. Macromol. Mater. Eng. 2018, 303, 1800169.
  18. Srinivas, V.; van Hooy-Corstjens, C.S.J.; Harings, J.A.W. Correlating Molecular and Crystallization Dynamics to Macroscopic Fusion and Thermodynamic Stability in Fused Deposition Modeling; a Model Study on Polylactides. Polymer 2018, 142, 348–355.
  19. Kitsakis, K.; Alabey, P.; Kechagias, J.; Vaxevanidis, N. A Study of the Dimensional Accuracy Obtained by Low Cost 3D Printing for Possible Application in Medicine. IOP Conf. Ser. Mater. Sci. Eng. 2016, 161, 12025.
  20. Aslani, K.-E.; Chaidas, D.; Kechagias, J.; Kyratsis, P.; Salonitis, K. Quality Performance Evaluation of Thin Walled PLA 3D Printed Parts Using the Taguchi Method and Grey Relational Analysis. J. Manuf. Mater. Process. 2020, 4, 47.
  21. Nathaphan, S.; Trutassanawin, W. Effects of Process Parameters on Compressive Property of FDM with ABS. Rapid Prototyp. J. 2021, 27, 905–917.
  22. Basavaraj, C.K.; Vishwas, M. Studies on Effect of Fused Deposition Modelling Process Parameters on Ultimate Tensile Strength and Dimensional Accuracy of Nylon. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Bangalore, India, 14–16 July 2016; Volume 149, pp. 1–11.
  23. Lalegani Dezaki, M.; Ariffin, M.K.; Serjouei, A.; Zolfagharian, A.; Hatami, S.; Bodaghi, M. Influence of Infill Patterns Generated by CAD and FDM 3D Printer on Surface Roughness and Tensile Strength Properties. Appl. Sci. 2021, 11, 7272.
  24. Padhi, S.K.; Sahu, R.K.; Mahapatra, S.S.; Das, H.C.; Sood, A.K.; Patro, B.; Mondal, A.K. Optimization of Fused Deposition Modeling Process Parameters Using a Fuzzy Inference System Coupled with Taguchi Philosophy. Adv. Manuf. 2017, 5, 231–242.
  25. Vahabli, E.; Rahmati, S. Improvement of FDM Parts’ Surface Quality Using Optimized Neural Networks—Medical Case Studies. Rapid Prototyp. J. 2017, 23, 825–842.
  26. Sai, T.; Pathak, V.K.; Srivastava, A.K. Modeling and Optimization of Fused Deposition Modeling (FDM) Process through Printing PLA Implants Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model and Whale Optimization Algorithm. J. Brazilian Soc. Mech. Sci. Eng. 2020, 42, 617.
  27. Vyavahare, S.; Kumar, S.; Panghal, D. Experimental Study of Surface Roughness, Dimensional Accuracy and Time of Fabrication of Parts Produced by Fused Deposition Modelling. Rapid Prototyp. J. 2020, 26, 1535–1554.
  28. Camposeco-Negrete, C. Optimization of FDM Parameters for Improving Part Quality, Productivity and Sustainability of the Process Using Taguchi Methodology and Desirability Approach. Prog. Addit. Manuf. 2020, 5, 59–65.
  29. Mohamed, O.A.; Masood, S.H.; Bhowmik, J.L. Modeling, Analysis, and Optimization of Dimensional Accuracy of FDM-Fabricated Parts Using Definitive Screening Design and Deep Learning Feedforward Artificial Neural Network. Adv. Manuf. 2021, 9, 115–129.
  30. Mohanty, A.; Nag, K.S.; Bagal, D.K.; Barua, A.; Jeet, S.; Mahapatra, S.S.; Cherkia, H. Parametric Optimization of Parameters Affecting Dimension Precision of FDM Printed Part Using Hybrid Taguchi-MARCOS-Nature Inspired Heuristic Optimization Technique. Mater. Today Proc. 2021, 50, 893–903.
  31. Garg, A.; Bhattacharya, A.; Batish, A. On Surface Finish and Dimensional Accuracy of FDM Parts after Cold Vapor Treatment. Mater. Manuf. Process. 2016, 31, 522–529.
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Subjects: Engineering, Manufacturing; Engineering, Industrial
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Muhammad Abas
,
Tufail Habib
,
Sahar Noor
,
Bashir Salah
,
Dominik Zimon
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Revisions: 2 times (View History)
Update Date: 26 Sep 2022
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