Title: Predicting Mechanical Properties of 3D Printed PLA Carbon Fiber Beyond Printing Parameter Dependencies

Authors: Mostafa Elnaggar, Madhura Athale , Taejoon Park , Koorosh Delavari, Reza Rezaei, Farhang Pourboghrat

DOI: 10.33599/nasampe/c.24.0297

Abstract: Advancements in 3D printing technology have ushered in innovative material combinations, such as PLA carbon fiber composites, offering superior mechanical properties. Despite these advancements, accurately predicting the mechanical characteristics of such materials remains a formidable challenge, often hinging on specific printing parameters. In this study, we propose employing an ensemble learning method to predict the tensile properties of 3D printed PLA carbon fiber, independently of the influence of printing parameters. Our methodology revolves around leveraging a machine learning technique that combines predictions from multiple models to enhance accuracy. We utilize a comprehensive dataset spanning a diverse spectrum of printing conditions and their corresponding mechanical outcomes. Each weak learner is trained on the dataset, providing predictions that are aggregated to form the ensemble model. Additionally, we employ a Grid search and a Bayesian model to optimize hyperparameters crucial to the machine learning algorithm and a genetic algorithm to optimize the printing parameters. Through these methods, we have successfully established a robust predictive model capable of extrapolating accurate predictions across various 3D printing scenarios, even under novel printing conditions. Notably, our model demonstrates resilience in predicting mechanical properties with minimal sensitivity to factors such as printing temperature, printing pattern, bed temperature, and printing speed.

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Conference: CAMX 2024 | San Diego CA

Publication Date: 2024/9/9

SKU: TP24-0000000297

Pages: 16

Price: $32.00