Influence of carbon nanotube inclusions to electrical, thermal, physical and mechanical behaviors of carbon-fiber-reinforced ABS composites

Akar, Alinda Öykü; Yıldız, Ümit Hakan; Tirkeş, Seha; Tayfun, Ümit; Hacıvelioğlu, Ferda

doi:10.1007/s42823-022-00332-y

Cited by 14 publications

(3 citation statements)

References 49 publications

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“…The GEP has several benefits but infrequently engages in the pitch of science materials [30,31]. Applications of GEP in materials engineering range from predicting the synthesis time of materials to modeling binding strength in polyethylene to simulating the hardness of nanocomposites.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Serrano,

Ulloa,

Flores

et al. 2024

MMEP

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The novel class of composite materials known as polyethylene-carbon nanotube composites (PECNTs) has attracted significant interest from scientists. In this study, authors investigated how artificial intelligence (AI) is employed to calculate the elastic modulus of PECNTs. For the first time, an AI-based modeling methodology replaces nanoindentation techniques like depth sensing indentation (DSI). This study highlights the complexities inherent in traditional methods, where the proposed methodology utilizes a gene expression programming (GEP) model, addressing challenges associated with accuracy in PECNT simulation. The proposed AI model test uses 135 input/output data pairs taken from the literature and randomly split into 82 training and 53 testing sets. The elastic modulus (EM) whichever dynamic E′ or quasi-static E) employs as an output factor in the models created, with the method of analysis, matrix type, processing technique, nanofiller type, and its content serving as inputs. Though the modeling progression is complete with results from the training and testing sets, the nanometer sensitivity of the prominent designs of the AI model displayed significant promise for the effective application of artificial intelligence methods in measuring the elastic modulus of PECNTs through non-destructive testing.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Serrano,

Ulloa,

Flores

et al. 2024

MMEP

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show abstract

“…Hence, the functionalized MWCNTs have been chosen in this work. Polypropylene and polystyrene, 1 polyurethane and polycarbonate, 29 polylactic acid, 30 acrylonitrile butadiene styrene 31 and polyethylene, 32 are some frequently used thermoplastics with CNTs. HDPE is a less expensive and the most extensively used industrial thermoplastic with impressive mechanical properties and various uses from daily life to numerous engineering sectors.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Buckling behavior of non‐uniformly heated 3D printed plain and functionally graded nanocomposites

et al. 2023

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The functionalized multi‐walled carbon nanotubes (MWCNTs) (0.5–5 wt.%) are compounded with high density polyethylene (HDPE), and, subsequently, used for extruding nanocomposite filaments to fabricate nanocomposites (NCs) and functionally graded nanocomposites (FGNCs) through 3D printing. The 3D printed NCs are investigated for coefficient of thermal expansion (CTE), and buckling under different non‐uniform temperature distributions (case‐1: left edge heating, case‐2: centre heating, and case‐3: left and right edge heating). A significant reduction in CTE is observed with MWCNT addition and gradation. The highest reduction in CTE is observed for H5 (5 wt.% of MWCNT in HDPE) NC and H1 ⟶ H3 ⟶ H5 (FGNC‐2) among the NCs and the FGNCs. It is noted that Tcr (critical buckling temperature) is highest for case‐3 and lowest for case‐2. The highest deflection is noticed in case‐2, while no significant difference is observed in case‐1 and case‐3 heating conditions. It is also observed that Tcr increases with gradation and MWCNTs addition. The H5 NC and FGNC‐2 exhibited the highest Tcr among the NCs and FGNCs, respectively. The maximum deflection is noticed for HDPE, whereas the minimum deflection is noticed for FGNC‐2 and H‐5 NC among the tested samples. The results also revealed that Tcr is very sensitive to type of heating.

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Carbon Nanostructures for Automotive and Aerospace Applications

Erol,

Mustafov,

Akalın

et al. 2023

Handbook of Functionalized Carbon Nanostructures

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Influence of carbon nanotube inclusions to electrical, thermal, physical and mechanical behaviors of carbon-fiber-reinforced ABS composites

Cited by 14 publications

References 49 publications

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Buckling behavior of non‐uniformly heated 3D printed plain and functionally graded nanocomposites

Carbon Nanostructures for Automotive and Aerospace Applications

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