The framework offers a data-driven way to optimize 3D-printed parts for lightness and flexibility without sacrificing necessary strength.
The internal structure of the 3D print (e.g., lattice, honeycomb, and linear). Infill Rates: Density levels ranging from 15% to 60% . 57533.rar
Lattice infill patterns were found to underperform compared to other structures in terms of tensile strength. The framework offers a data-driven way to optimize
The study utilized Copula-based data augmentation to generate 20,000 synthetic data points to improve the accuracy of their machine learning models. Machine Learning Models Used Lattice infill patterns were found to underperform compared
The research focuses on predicting the of 3D-printed Polylactic Acid (PLA) components under various conditions. This is critical for industrial applications where the strength of a part can change based on its internal structure and how it is printed. Key Technical Variables
The researchers compared several algorithms to determine which could best predict the strength of the printed parts: . Artificial Neural Networks (ANN) . Main Findings
Structural orientation along the x, y, and z axes.