Abstract

The realm of 3D printing technology, also referred to as additive manufacturing, has garnered increasing interest in recent times due to its capacity to construct intricate geometric structures. Fused deposition modeling (FDM) stands out among the various techniques and has gained widespread adoption. However, achieving optimal outcomes with FDM poses a challenge, necessitating meticulous selection of process parameters. Presently, many methodologies rely on Machine Learning (ML) algorithms akin to open-loop systems, offering predictions on printed part properties but lacking quality assurance mechanisms. Conversely, certain closed-loop approaches focus on monitoring a single adjustable processing parameter to assess printed part properties. This study aims to investigate the influence of process parameters and control techniques on mechanical strength, tribology, and other output parameters of production. By providing a comprehensive overview of these developed methods, it aims to facilitate comparison regarding their characteristics, merits, and drawbacks, aiding in the selection of the most suitable approach for specific applications.

Keywords:3-D printer, FDM, machine learning, control system, finite element analysis

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1. Feng Q, Maier W, Möhring H. Application of machine learning to optimize process parameters in fused deposition modeling of PEEK material. Procedia CIRP. 2022;107:1–8. Available from: https://doi.org/10.1016/j.procir.2022.04.001
2. Geng P, Zhao J, Gao Z, Wu W, Ye W, Li G, et al. Effects of printing parameters on the mechanical properties of high-performance polyphenylene sulfide three-dimensional printing. 3D Printing and Additive Manufacturing, 2021;8(1):33–41. Available from: https://doi.org/10.1089/3dp.2020.0052
3. Zhen H, Zhao B, Quan L, Fu J. Effect of 3D printing process parameters and heat treatment conditions on the mechanical properties and microstructure of PEEK parts. Polymers 2023;15: 2209. https://doi.org/10.3390/polym15092209
4. Li H, Wang T, Sun J, Yu Z. The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties. Rapid Prototype J. 2018;24:80–92.
5. Seppala JE, Han SH, Hillgartner KE, Davis CS. Weld formation during material extrusion additive manufacturing. Soft Matter; 2017.
6. Ayrılmış, N. Effect of layer thickness on surface properties of 3D printed materials produced from wood flour/PLA filament. Polym Test. 2018;71:163–166. Available from: https://doi.org/10.1016/j.polymertesting.2018.09.009
7. Motaparti KP, Taylor G, Leu MC, Chandrashekhara K, Castle J, Matlack M. Effects of build parameters on compression properties for ULTEM 9085 parts by fused deposition modeling. 2016. https://repositories.lib.utexas.edu/items/dca47cee-c98f-425b-bd3d-fc6cb9f1bd51
8. Gonabadi H, Chen Y, Yadav A, Bull S. Investigation of the effect of raster angle, build orientation, and infill density on the elastic response of 3D printed parts using finite element microstructural modeling and homogenization techniques. Int J Adv Manuf Technol. 2021;118(5–6):1485–1510. Available from: https://doi.org/10.1007/s00170-021-07940-4
9. Bakhtiari, H.; Aamir, M.; Tolouei-Rad, M. Effect of 3D Printing Parameters on the Fatigue Properties of Parts Manufactured by Fused Filament Fabrication: A Review. Appl. Sci. 2023;13:904. Available from: https://doi.org/10.3390/app13020904
10. Qayyum H, Hussain G, Sulaiman M, Hassan M, Ali A, Muhammad R, et al. Effect of raster angle and infill pattern on the in-plane and edgewise flexural properties of fused filament fabricated acrylonitrile–butadiene–styrene. 2022;12(24):12690. Available from: https://doi.org/10.3390/app122412690
11. Tang S, Cheang P, AbuBakar M, Khor K, Liao K. Tension–tension fatigue behavior of hydroxyapatite reinforced polyetheretherketone composites. Int J Fatigue. 2004;26:49–57.
12. Mura A, Ricci A, Canavese G. Investigation of fatigue behavior of ABS and PC-ABS polymers at different temperatures. Materials. 2018;11:1818.
13. Pruitt Fatigue Testing and Behavior of Plastics; ASM International: Materials Park, OH, USA; 2000. pp. 758–767.
14. Sood AK, RK Ohdar, SS Mahapatra. Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J Adv Res. 2012;3(1):81–90.
15. Sood AK, A Equbal, V Toppo, RK Ohdar, SS Mahapatra. An investigation on sliding wear of FDM built parts. CIRP J Manufac Sci Technol. 2012;5(1):48–54.
16. Vijayaraghavan V, Garg A, Lam JSL, Panda B, Mahapatra SS. Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. Int J Adv Manuf Technol. 2014;78(5–8):781–793.
17. Vosniakos G-C, Maroulis T, Pantelis D. A method for optimizing process parameters in layer-based rapid prototyping. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf. 2007;221(8):1329–1340.
18. Equbal A, Sood AK, Mahapatra SS. Prediction of dimensional accuracy in fused deposition modelling- a fuzzy logic approach. Int J Prod Qual Manage. 2011;7(1):22–43.
19. Sood AK, Ohdar RK, Mahapatra SS. Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manufac. 2009;224(1):135–145.
20. Li Z, Zhang Z, Shi J, Wu D. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing. 2019;57:488–495.
21. Noriega, A, Blanco D, Alvarez BJ, Garcia A. Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manufac Technol. 2013;69(9–12):2301–2313.
22. Jiang Z, Liu Y, Chen H, Hu Q. Optimization of Process Parameters for Biological 3D Printing Forming Based on BP Neural Network and Genetic Algorithm. 21st ISPE Inc. International Conference on Concurrent Engineering; 2014.
23. Mohamed OA, Masood SH, Bhowmik JL. Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing. Advances in Production Engineering & Management. 2016;11(3):227–238.
24. Bayraktar Ö, G. Uzun, R. Çakiroğlu, A. Guldas. Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks. Polymers for Advanced Technologies. 2017;28(8):1044–1051.
25. Vahabli E, S. Rahmati. Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Precision Eng Manufac. 2016;17(12):1589–1603.
26. Papazetis G, Vosniakos GC. Mapping of deposition-stable and defect-free additive manufacturing via material extrusion from minimal experiments. Int J Adv Manuf Technol. 2019;100:2207–2219. https://doi.org/10.1007/s00170-018-2820-1

Abstract

The realm of 3D printing technology, also referred to as additive manufacturing, has garnered increasing interest in recent times due to its capacity to construct intricate geometric structures. Fused deposition modeling (FDM) stands out among the various techniques and has gained widespread adoption. However, achieving optimal outcomes with FDM poses a challenge, necessitating meticulous selection of process parameters. Presently, many methodologies rely on Machine Learning (ML) algorithms akin to open-loop systems, offering predictions on printed part properties but lacking quality assurance mechanisms. Conversely, certain closed-loop approaches focus on monitoring a single adjustable processing parameter to assess printed part properties. This study aims to investigate the influence of process parameters and control techniques on mechanical strength, tribology, and other output parameters of production. By providing a comprehensive overview of these developed methods, it aims to facilitate comparison regarding their characteristics, merits, and drawbacks, aiding in the selection of the most suitable approach for specific applications.

Keywords:3-D printer, FDM, machine learning, control system, finite element analysis

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Feng Q, Maier W, Möhring H. Application of machine learning to optimize process parameters in fused deposition modeling of PEEK material. Procedia CIRP. 2022;107:1–8. Available from: https://doi.org/10.1016/j.procir.2022.04.001
2. Geng P, Zhao J, Gao Z, Wu W, Ye W, Li G, et al. Effects of printing parameters on the mechanical properties of high-performance polyphenylene sulfide three-dimensional printing. 3D Printing and Additive Manufacturing, 2021;8(1):33–41. Available from: https://doi.org/10.1089/3dp.2020.0052
3. Zhen H, Zhao B, Quan L, Fu J. Effect of 3D printing process parameters and heat treatment conditions on the mechanical properties and microstructure of PEEK parts. Polymers 2023;15: 2209. https://doi.org/10.3390/polym15092209
4. Li H, Wang T, Sun J, Yu Z. The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties. Rapid Prototype J. 2018;24:80–92.
5. Seppala JE, Han SH, Hillgartner KE, Davis CS. Weld formation during material extrusion additive manufacturing. Soft Matter; 2017.
6. Ayrılmış, N. Effect of layer thickness on surface properties of 3D printed materials produced from wood flour/PLA filament. Polym Test. 2018;71:163–166. Available from: https://doi.org/10.1016/j.polymertesting.2018.09.009
7. Motaparti KP, Taylor G, Leu MC, Chandrashekhara K, Castle J, Matlack M. Effects of build parameters on compression properties for ULTEM 9085 parts by fused deposition modeling. 2016. https://repositories.lib.utexas.edu/items/dca47cee-c98f-425b-bd3d-fc6cb9f1bd51
8. Gonabadi H, Chen Y, Yadav A, Bull S. Investigation of the effect of raster angle, build orientation, and infill density on the elastic response of 3D printed parts using finite element microstructural modeling and homogenization techniques. Int J Adv Manuf Technol. 2021;118(5–6):1485–1510. Available from: https://doi.org/10.1007/s00170-021-07940-4
9. Bakhtiari, H.; Aamir, M.; Tolouei-Rad, M. Effect of 3D Printing Parameters on the Fatigue Properties of Parts Manufactured by Fused Filament Fabrication: A Review. Appl. Sci. 2023;13:904. Available from: https://doi.org/10.3390/app13020904
10. Qayyum H, Hussain G, Sulaiman M, Hassan M, Ali A, Muhammad R, et al. Effect of raster angle and infill pattern on the in-plane and edgewise flexural properties of fused filament fabricated acrylonitrile–butadiene–styrene. 2022;12(24):12690. Available from: https://doi.org/10.3390/app122412690
11. Tang S, Cheang P, AbuBakar M, Khor K, Liao K. Tension–tension fatigue behavior of hydroxyapatite reinforced polyetheretherketone composites. Int J Fatigue. 2004;26:49–57.
12. Mura A, Ricci A, Canavese G. Investigation of fatigue behavior of ABS and PC-ABS polymers at different temperatures. Materials. 2018;11:1818.
13. Pruitt Fatigue Testing and Behavior of Plastics; ASM International: Materials Park, OH, USA; 2000. pp. 758–767.
14. Sood AK, RK Ohdar, SS Mahapatra. Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J Adv Res. 2012;3(1):81–90.
15. Sood AK, A Equbal, V Toppo, RK Ohdar, SS Mahapatra. An investigation on sliding wear of FDM built parts. CIRP J Manufac Sci Technol. 2012;5(1):48–54.
16. Vijayaraghavan V, Garg A, Lam JSL, Panda B, Mahapatra SS. Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. Int J Adv Manuf Technol. 2014;78(5–8):781–793.
17. Vosniakos G-C, Maroulis T, Pantelis D. A method for optimizing process parameters in layer-based rapid prototyping. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf. 2007;221(8):1329–1340.
18. Equbal A, Sood AK, Mahapatra SS. Prediction of dimensional accuracy in fused deposition modelling- a fuzzy logic approach. Int J Prod Qual Manage. 2011;7(1):22–43.
19. Sood AK, Ohdar RK, Mahapatra SS. Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manufac. 2009;224(1):135–145.
20. Li Z, Zhang Z, Shi J, Wu D. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing. 2019;57:488–495.
21. Noriega, A, Blanco D, Alvarez BJ, Garcia A. Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manufac Technol. 2013;69(9–12):2301–2313.
22. Jiang Z, Liu Y, Chen H, Hu Q. Optimization of Process Parameters for Biological 3D Printing Forming Based on BP Neural Network and Genetic Algorithm. 21st ISPE Inc. International Conference on Concurrent Engineering; 2014.
23. Mohamed OA, Masood SH, Bhowmik JL. Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing. Advances in Production Engineering & Management. 2016;11(3):227–238.
24. Bayraktar Ö, G. Uzun, R. Çakiroğlu, A. Guldas. Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks. Polymers for Advanced Technologies. 2017;28(8):1044–1051.
25. Vahabli E, S. Rahmati. Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Precision Eng Manufac. 2016;17(12):1589–1603.
26. Papazetis G, Vosniakos GC. Mapping of deposition-stable and defect-free additive manufacturing via material extrusion from minimal experiments. Int J Adv Manuf Technol. 2019;100:2207–2219. https://doi.org/10.1007/s00170-018-2820-1