Application of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts

[ X ]

Tarih

2024

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Wiley

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 mu m. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method. image

Açıklama

Anahtar Kelimeler

3D printing, additive manufacturing, ANOVA, artificial neural network, dimensional accuracy, polylactic acid, Taguchi method

Kaynak

Journal of Polymer Science

WoS Q Değeri

N/A

Scopus Q Değeri

Q2

Cilt

62

Sayı

9

Künye