Faster and Stronger 3D Printed Parts

This research addresses optimizing infill patterns in 3D printed objects to maximize structural strength relative to weight. The team developed three distinct algorithmic approaches to achieve this goal.

The study implemented three methods:

1. Area-based optimization - adjusts infill density according to section area relative to total layer area 2. Simulation-based infill - determines density using computational simulation data 3. Generative support structures - creates infill patterns based on part geometry and stress concentration points

The researchers tested parts using PLA filament through compression, torque, and tension assessments. The area based algorithm had mixed results that greatly varied on the design of the shape and the precision of the voxel meshing value. The simulation-based approach proved more consistent, while the generative support structure method showed theoretical promise for optimal strength-to-weight ratios, though it demands significant computational resources.

Proposed enhancements include material flexibility calculations, reduced computational complexity for generative algorithms, and integration of deep learning with physics-based modeling.

Collaborator: Andrew Dang