A Machine Learning Approach for Predicting Flexural Strength of 3D Printed Hexagon Lattice-Cored Sandwich Structures

Abstract

The development of sandwich structures using Fused Filament Fabrication (FFF) technology is an effective method for the rapid construction of complex profiles and is widely recognized for various structural applications. In this study, hexagon lattice-cored sandwich structures are developed by placing the lattice core in the center portion of the PLA polymeric specimen. The performance is analyzed with respect to varying levels of 3D-Printing Factors (3D-PFs) such as Nozzle Temperature (NT), Line Width (LW), Printing Speed (PS), and Layer Height (LH). The levels of the respective 3D-PFs are varied as follows: NT (180, 190, 200, and 210 ℃), LH (0.15, 0.2, 0.25, and 0.3mm), PS (15, 20, 25, and 30mm/sec), and LW (0.1, 0.2, 0.3, and 0.4mm). The sandwich flexural samples are 3D printed using an FFF 3D printer, and their flexural properties are examined using a Universal Testing Machine (UTM). In the present work, various Machine Learning (ML) algorithms, such as Least Angle Regression (LARS), Support Vector Machine (SVM), Decision Tree (DT), and Ridge Regression (Ridge), are employed to predict the Flexural Strength (FS) of the 3D-printed sandwich structures and help in determining the optimized levels of 3D-PFs for achieving higher FS. The findings indicate that the LARS algorithm, particularly when applied within the Bagging Ensemble Learning Technique (ELT), exhibits the highest precision, showcasing a Mean Absolute Error (MAE) of 0.163, Root Mean Square Error (RMSE) of 0.153, and Median Absolute Error (MedAE) of 0.129. With the help of the LARS algorithm under Bagging ELT, the impact of 3D-PFs on the resulting FS is analyzed, which helps in determining the optimized levels of 3D-PFs concerning FS. The sandwich structures fabricated at the optimized levels show improved flexural properties, making them suitable for various structural applications.