| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Design of Structural Materials
|
| Presentation Title |
Multi-objective Lattice Optimization Using an Efficient Neural Network Approach |
| Author(s) |
Anthony Garland, Ben White, Brad Boyce, Ryan Alberdi |
| On-Site Speaker (Planned) |
Anthony Garland |
| Abstract Scope |
Additively manufactured lattice metamaterials offer the ability to design effective material properties tailored to meet specific engineering requirements. However, the optimal design of such lattices is currently limited in a number of ways: (1) most topology optimization methods are restricted to linear phenomena such as elasticity, (2) explicit finite element representation and contribution toward the objective of every feature is computationally expensive or intractable, (3) intermediate densities between material and void create computational instabilities, and (4) the optimization rarely takes into consideration manufacturing limits. To address these challenges, a convolutional neural network (NN) was trained on thousands of simulations of random and rational unit cell designs. The NN is differentiable and serves as an efficient surrogate model in place of expensive finite element simulations. In this presentation, we explore gradient optimization of multiple competing objectives on a pareto front, including explicit dynamic shock response, stiffness, and manufacturability constraints. |
| Proceedings Inclusion? |
Planned: |