Review on multi-objective optimization of FDM process parameters for composite materials

Patel, Rahul; Jani, Suketu; Joshi, Ankita

doi:10.1007/s12008-022-01111-9

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…This metric was used to screen tens of thousands of all‐silica zeolites, which were taken from the International Zeolite Association database and the Predicted Crystallography Open Database. Multi‐objective optimization (MOO) is an appealing approach in material science that has been used in many experimental studies [98–100] . However, its application is scarce in HTC.…”

Section: Advances In Htc Techniquesmentioning

confidence: 99%

Advances in data‐assisted high‐throughput computations for material design

Xu,

Zhang,

Huo

et al. 2023

Materials Genome Engineering Advances

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Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development. The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations. Because of their numerous variables in material design, however, the variable space is still too large to be accessed thoroughly even with a computational approach. High‐throughput computations (HTC) make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic, robust, and concurrent streamlines. The efficiency of HTC, which is one of the pillars of materials genome engineering, has been verified in many studies, but its applications are still limited by demanding computational costs. Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem. In the past years, many studies have focused on the development and application of HTC and data combined approaches, which is considered as a new paradigm in computational materials science. This review focuses on the main advances in the field of data‐assisted HTC for material research and development and provides our outlook on its future development.

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Section: Advances In Htc Techniquesmentioning

confidence: 99%

Advances in data‐assisted high‐throughput computations for material design

Xu,

Zhang,

Huo

et al. 2023

Materials Genome Engineering Advances

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“…45,46 There are limited studies in the literature regarding the dimensional accuracy of 3D products fabricated using the FDM technique and the effect of printing parameters on this. 47,48 Hanon et al examined the effect of printing parameters on the dimensional accuracy of samples produced with PLA material. 49 Cylindrical and dog-bone tensile test samples were produced using different printing parameters based on FDM, including build orientation, raster direction angle, and layer thickness.…”

Section: Introductionmentioning

confidence: 99%

“…There are limited studies in the literature regarding the dimensional accuracy of 3D products fabricated using the FDM technique and the effect of printing parameters on this 47,48 . Hanon et al examined the effect of printing parameters on the dimensional accuracy of samples produced with PLA material 49 .…”

Section: Introductionmentioning

confidence: 99%

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

Gunes,

Ulkir,

Kuncan

2024

Journal of Polymer Science

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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 μm. 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.

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