Comparison of Models to Predict Mechanical Properties of FR-AM Composites and a Fractographical Study

• Published in: Polymers Vol. 14; no. 17; p. 3546
• Main Authors: Leon-Becerra, Juan, González-Estrada, Octavio Andrés, Sánchez-Acevedo, Heller
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.08.2022; MDPI
• Subjects: 3D printing; Additive manufacturing; Algorithms; Artificial intelligence; Carbon fibers; Composite materials; Continuous fiber composites; Data mining; Deep learning; Engineering Sciences; Experiments; Failure mechanisms; Failure surface; Fiber reinforced polymers; Gaussian process; Machine learning; Mechanical properties; Mechanics; Model accuracy; Neural networks; Polymers; Prediction models; Process parameters; Regression models; Stiffness; Structural mechanics; Tensile tests; Topology; fractographic analysis; additive manufacturing; machine learning; thermoplastic composites; micromechanics
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym14173546

Continuous fiber-reinforced additive manufacturing (cFRAM) composites improve the mechanical properties of polymer components. Given the recent interest in their mechanical performance and failure mechanisms, this work aims to describe the principal failure mechanisms and compare the prediction capabilities for the mechanical properties, stiffness constants, and strength of cFRAM using two distinct predictive models. This work presents experimental tensile tests of continuous carbon fiber AM composites varying their reinforced fraction, printing direction, and fiber angle. In the first predictive model, a micromechanical-based model for stiffness and strength predicts their macroscopic response. In the second part, data-driven models using different machine learning algorithms for regression are trained to predict stiffness and strength based on critical parameters. Both models are assessed regarding their accuracy, ease of implementation, and generalization capabilities. Moreover, microstructural images are used for a qualitative evaluation of the parameters and their influence on the macroscopic response and failure surface topology. Finally, we conclude that although predicting the mechanical properties of cFRAM is a complex task, it can be carried on a Gaussian process regression and a micromechanical model, with good accuracy generalized onto different process parameters specimens.