Title: Machine Learning Enhanced Material Models for Composites Process Simulation

Authors: Göran Fernlund, Kamyar Gordnian, Oskar Fernlund, Anoush Poursartip

DOI: 10.33599/nasampe/s.24.0070

Abstract: When simulating composites processes it is essential to include all components and features of the system: material, shape, tooling, equipment, and process. Material properties of the composite change from liquid to solid during the process, and it is critical to perform materials characterization and capture the evolving material behaviour in mathematical form. Creating an accurate mathematical description of material properties is challenging due to the highly non-linear material behaviour and the difficulty of generating high quality data. This paper specifically addresses generation of cure kinetics models for thermoset composites, but the method presented is applicable to other materials and properties. The traditional approach to generating cure kinetics models is to perform dynamic scanning calorimeter (DSC) tests and fit a simple physics-inspired parametric cure kinetic model to data. However, this approach scales poorly for complex material behaviour and in diffusion-controlled regions of the reaction. This paper presents a machine learning enhanced approach for fitting cure kinetics models to DSC data. The outcome is highly accurate and robust material models. Comparison of the proposed method to traditional methods is presented for the high-performance epoxy matrix CYCOM® EP2190 from Syensqo.

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Conference: SAMPE 2024

Publication Date: 2024/05/20

SKU: TP24-0000000070

Pages: 14

Price: $28.00