Transparent Organic-Inorganic Hybrids Obtained from Covalently Bonded Ureasilicate Monomers: Optical and Mechanical Properties

Ilcheva, V.; Boev, V. I.; Zamfirova, Galina; Gaydarov, Valentin; Lilova, Vanya; Petkova, T.

doi:10.1007/978-94-024-2018-0_4

Cited by 1 publication

(1 citation statement)

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“…𝐸: elastic modulus f the material 𝑣: Poisson's ratio of the material 𝐸 𝑖 : indenter of elastic modulus 𝑣 𝑖 : indenter of Poisson's ratio The Oliver and Pharr method overestimates the contact area and underestimates the modulus of elasticity when used to pileup materials (where the pile-up is constrained) [37].…”

Section: Analysis Of Methodsmentioning

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: Analysis Of Methodsmentioning

confidence: 99%