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


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

April 24, 2022
June 29, 2022
November 20, 2022
artificial intelligence, neural network, additive manufacturing, 3D printing, tensile strength

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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Last updated on Jun. 19, 2024