Study of Polymor Properties Prediction Using Nonlinear SEM Based on Gaussian Process Regression


KIPS Transactions on Computer and Communication Systems, Vol. 13, No. 1, pp. 1-9, Jan. 2024
https://doi.org/10.3745/KTCCS.2024.13.1.1,   PDF Download:
Keywords: Gaussian Process Regression, structural equation modeling, Multivariate analysis, Polymer Development, Polyacetal(PA)
Abstract

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.


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Cite this article
[IEEE Style]
M. Kyung-Yeol and P. Kun-Wook, "Study of Polymor Properties Prediction Using Nonlinear SEM Based on Gaussian Process Regression," KIPS Transactions on Computer and Communication Systems, vol. 13, no. 1, pp. 1-9, 2024. DOI: https://doi.org/10.3745/KTCCS.2024.13.1.1.

[ACM Style]
Moon Kyung-Yeol and Park Kun-Wook. 2024. Study of Polymor Properties Prediction Using Nonlinear SEM Based on Gaussian Process Regression. KIPS Transactions on Computer and Communication Systems, 13, 1, (2024), 1-9. DOI: https://doi.org/10.3745/KTCCS.2024.13.1.1.