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JACIII Vol.26 No.6 pp. 952-958
doi: 10.20965/jaciii.2022.p0952
(2022)

Paper:

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Regression Model for Optimization and Prediction of Tensile Strength of a PLA Prototype Printed

Lahcen Hamouti†, Omar El Farissi, and Omar Outemssa

National School of Applied Sciences-Agadir, Ibn Zohr University
BP 1136, CP 80000 Agadir, Morocco

†Corresponding author

Received:
April 24, 2022
Accepted:
June 29, 2022
Published:
November 20, 2022
Creative Commons License
Keywords:
artificial intelligence, neural network, additive manufacturing, 3D printing, tensile strength
Abstract

The experimental studies on prototypes printed in 3D with polylactic acid (PLA) material still seek to characterize the mechanical behavior and the deformations of these printed samples according to the various solicitations. The huge number of parameters intervening in these properties makes the control of process difficult and expensive. Previous studies on the impact of these parameters on the mechanical properties are limited to the investigation of a very less number of parameters. The objective of the present study is to take advantage of artificial intelligence tools, and to exploit the experimental results, in order to present artificial models that are able to optimize the choice of parameters intervening in the properties (tensile strength) of printed parts.

Cite this article as:
L. Hamouti, O. El Farissi, and O. Outemssa, “Regression Model for Optimization and Prediction of Tensile Strength of a PLA Prototype Printed,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 952-958, 2022.
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References
  1. [1] D. J. S. Agron, C. I. Nwakanma, J. Lee, and D. Kim, “Smart Monitoring for SLA-type 3D Printer using Artificial Neural Network,” 2020 Korean Institute of Communication and Sciences (KICS) Summer Conf., Vol.72, pp. 1203-1204, 2020.
  2. [2] S. Bhagia et al., “Tensile properties of 3D-printed wood-filled PLA materials using poplar trees,” Appl. Mater. Today, Vol.21, 100832, doi: 10.1016/j.apmt.2020.100832, 2020.
  3. [3] D. Yadav, D. Chhabra, R. Kumar, A. Ahlawat, and A. Phogat, “Optimization of FDM 3D printing process parameters for multi-material using artificial neural network,” Mater. Today Proc., Vol.21, pp. 1583-1591, doi: 10.1016/j.matpr.2019.11.225, 2020.
  4. [4] M. Pant and R. M. Singari, “Wear assessment of 3-D printed parts of PLA (polylactic acid) using Taguchi design and Artificial Neural Network (ANN) technique,” Materials Research Express, Vol.7, No.11, 115307, 2020.
  5. [5] V. E. Kuznetsov, A. Solonin, O. D. Urzhumtsev, and R. Schilling, “Strength of PLA Components Fabricated with Fused Deposition Technology Using a Desktop 3D Printer as a Function of Geometrical Parameters of the Process,” Polymers, Vol.10, No.3, pp. 1-16, doi: 10.20944/preprints201803.0036.v1, 2018.
  6. [6] J. M. Chacón, M. A. Caminero, E. García-Plaza, and P. J. Núñez, “Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection,” Mater. Des., Vol.124, pp. 143-157, doi: 10.1016/j.matdes.2017.03.065, 2017.
  7. [7] L. P. T. Huynh, H. A. Nguyen, H. Q. Nguyen, L. K. H. Phan, and T. T. Tran, “Effect of process parameters on mechanical strength of fabricated parts using the fused deposition modelling method,” J. Korean Soc. Precis. Eng., Vol.36, No.8, pp. 705-712, doi: 10.7736/KSPE.2019.36.8.705, 2019.
  8. [8] R. V. Pazhamannil, P. Govindan, and P. Sooraj, “Prediction of the tensile strength of polylactic acid fused deposition models using artificial neural network technique,” Mater. Today Proc., Vol.46, Part 19, pp. 9187-9193, doi: 10.1016/j.matpr.2020.01.199, 2019.
  9. [9] E. E. Cho, H. H. Hein, Z. Lynn, S. J. Hla, and T. Tran, “Investigation on Influence of Infill Pattern and Layer Thickness on Mechanical Strength of PLA Material in 3D Printing Technology,” J. Eng. Sci. Res, Vol.3, No.2, pp. 27-37, doi: 10.26666/rmp.jesr.2019.2.5, 2019.
  10. [10] M. M. Hanon, L. Zsidai, and Q. Ma, “Accuracy investigation of 3D printed PLA with various process parameters and different colors,” Mater. Today Proc., Vol.42, pp. 3089-3096, doi: 10.1016/j.matpr.2020.12.1246, 2021.
  11. [11] Y. Tao, P. Li, and L. Pan, “Improving tensile properties of polylactic acid parts by adjusting printing parameters of open source 3D printers,” Medziagotyra, Vol.26, No.1, pp. 83-87, doi: 10.5755/j01.ms.26.1.20952, 2020.
  12. [12] A. J. Sheoran and H. Kumar, “Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: Review and reflection on present research,” Mater. Today Proc., Vol.21, pp. 1659-1672, doi: 10.1016/j.matpr.2019.11.296, 2019.
  13. [13] M. Á. Caminero, J. M. Chacón, E. García-Plaza, P. J. Núñez, J. M. Reverte, and J. P. Becar, “Additive manufacturing of PLA-based composites using fused filament fabrication: Effect of graphene nanoplatelet reinforcement on mechanical properties, dimensional accuracy and texture,” Polymers (Basel), Vol.11, No.5, doi: 10.3390/polym11050799, 2019.
  14. [14] A. Kaptan and F. Kartal, “The Effect of Fill Rate on Mechanical Properties of PLA Printed Samples,” J. Inst. Sci. Technol., Vol.10, No.3, pp. 1919-1927, doi: 10.21597/jist.706003, 2020.
  15. [15] M. R. Ayatollahi, A. Nabavi-Kivi, B. Bahrami, M. Y. Yahya, and M. R. Khosravani, “The influence of in-plane raster angle on tensile and fracture strengths of 3D-printed PLA specimens,” Eng. Fract. Mech., Vol.237, 107225, doi: 10.1016/j.engfracmech.2020.107225, 2020.
  16. [16] H. Gonabadi, A. Yadav, and S. J. Bull, “The effect of processing parameters on the mechanical characteristics of PLA produced by a 3D FFF printer,” Int. J. Adv. Manuf. Technol., Vol.111, Nos.3-4, pp. 695-709, doi: 10.1007/s00170-020-06138-4, 2020.
  17. [17] T. D. Harpool, “Observing the Effects of Infill Shapes on the Tensile Characteristic of 3D printed plastic parts,” Master’s Theses, Wichita State University, pp. 1-88, 2016.
  18. [18] A. Alafaghani et al., “Experimental Optimization of Fused Deposition Modelling Processing Parameters: A Design-For-Manufacturing Approach,” Procedia Manufacturing, Vol.10, pp. 791-803, doi: 10.1016/j.promfg.2017.07.079, 2017.
  19. [19] M. Behzadnasab and A. Yousefi, “Effects of 3D Printer Nozzle Head Temperature on the Physical and Mechanical Properties of PLA Based Product,” Proc. of the 12th Int. Seminar on Polymer Science and Technology, pp. 3-5, 2016.
  20. [20] F. Johansson, “Optimizing Fused Filament Fabrication 3D Printing for Durability: Tensile Properties and Layer Bonding,” Master’s Degree Thesis Mechanical Engineering, Blekinge Institute of Technology, pp. 1-87, 2016.
  21. [21] G. Ćwikł a, C. Grabowik, K. Kalinowski, I. Paprocka, and P. Ociepka, “The influence of printing parameters on selected mechanical properties of FDM/FFF 3D-printed parts,” IOP Conf. Ser. Mater. Sci. Eng., Vol.227, No.1, doi: 10.1088/1757-899X/227/1/012033, 2017.
  22. [22] C. Abeykoon, P. Sri-Amphorn, and A. Fernando, “Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures,” Int. J. Light. Mater. Manuf., Vol.3, No.3, pp. 284-297, doi: 10.1016/j.ijlmm.2020.03.003, 2020.
  23. [23] I. Rojek, D. Mikoł ajewski, E. Dostatni, and M. Macko, “AI-Optimized Technological Aspects of the Material Used in 3D Printing Processes for Selected Medical Applications,” Materials, Vol.13, No.23, Article No.5437, 2020.

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Last updated on Apr. 18, 2024