Design Complexity

Functionally-Graded Materials

AM’s material complexity enables designers to locally vary material distributions throughout parts created on certain processes.  For material jetting, parts can can be designed 40 microns at a time through voxel-based modelling.  This enables the creation of both highly tailored digital composites, as well as functionally-graded materials (FGMs) that exhibit different material properties throughout a part’s volume.  This project is investigating how microstructural design decisions affect the macroscale properties of printed digital composites and how these composites can be used to predict the behavior of printed FGMs.

Research Team

Dorcas Kaweesa

Sponsors & Partners

Stratasys

Beese Research Group

Design Complexity

Machine Learning With Voxel-Based Design

Due to the design complexity achievable with AM, it can often be challenging for novice designers to fully consider DfAM opportunities and restrictions in part design.  By using machine learning, it may be possible to leverage digital design repositories to automate certain aspects of DfAM, which may in turn enhance designer’s ability to take advantage of cutting-edge manufacturing capabilities.  This project is investigating whether machine learning can be used to (1) provide real-time design insight as engineers apply DfAM concepts in their parts and (2) realize previously incomprehensible designs for AM, such as smooth transition regions between drastically different lattice structures.

Research Team

Glen Williams

Daniel Spillane

Chris McComb

Tim Simpson

Sponsors & Partners

National Science Foundation

THRED Group