Predicting mechanical strength and optimized parameters in FDM-printed polylactic acid parts via artificial neural networks and desirability analysis

• Autorzy: Abdulridha HInd H. , Abbas Tahseen F. , Bedan Aqeel S.

Identyfikatory

DOI

10.2478/mspe-2024-0040

• Języki publikacji: EN

Abstrakty

EN

Fused deposition modeling (FDM) is a commonly used additive manufacturing (AM) technique in both domestic and industrial end-product fabrications. It produces prototypes and parts with complex geometric designs, which has the major benefits of eliminating the need for expensive tooling and flexibility. However, the produced parts often face poor part strength due to anisotropic fabrication strategies. The printing procedure, the kind of material utilized, and the printing parameters all have a significant impact on the mechanical characteristics of the printed item. In order to predict the mechanical properties related to printed components made with the use of FDM and Polylactic Acid (PLA) material, this study concentrates on developing a prediction model utilizing Artificial Neural Networks (ANNs). This study used the Taguchi design of experiments technique, utilizing (L25) orthogonal array as well as a Neural Network (NN) method with two layers and 15 neurons. The effect of FDM parameters (layer thickness (mm), percentage of infill density, number of top/bottom layers, shell thickness (mm), and infill overlap percentage) on ultimate tensile and compressive strength (UTS and UCS) was examined through analysis of variance (ANOVA). With an ANOVA result of 67.183% and 40.198%, respectively, infill density percentage was found to be the most significant factor influencing UCS and UTS dependent on other parameters. The predicted results demonstrated valuable agreement with experimental values, with mean squared errors of (0.098) and (0.326) for UTS and UCS, respectively. The predictive model produces flexibility in selecting the optimal setting based on applications.

Słowa kluczowe

EN

FDM; PLA; process parameters; ANOVA; ANN

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2024
• Tom: nr 3 (32)
• Strony: 428--437
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Abdulridha HInd H.

• University of Technology Production Engineering and Metallurgy Department Baghdad, Iraq

• https://orcid.org/0000-0003-4653-6073

autor

Abbas Tahseen F.

• University of Technology Production Engineering and Metallurgy Department Baghdad, Iraq

autor

Bedan Aqeel S.

• University of Technology Production Engineering and Metallurgy Department Baghdad, Iraq

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).