About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Gaussian Process Ensemble Active Learning for Autonomous Parameterization of Direct Ink Write 3D Printing |
Author(s) |
Erick Braham, Marshall Johnson, Surya Kalidindi, James Hardin |
On-Site Speaker (Planned) |
Erick Braham |
Abstract Scope |
At the frontier of additive manufacturing research there is a challenge to make complex geometries while incorporating new materials and methods into the fabrication process. Gaining precise control over a material’s behavior when introduced to a new process is often a trial and error process led by a human’s intuition. Can machines develop an “intuition” and how much data does such a process require? To answer these questions, we are developing an automated rapid exploration protocol for enabling a machine to gain its own “intuition”. This work proposes a multiple-gaussian-process based automated parameterization demonstrated with the direct ink write printing of freestanding structures. This case study examines several factors crucial to the success of this automation including efficiency, design space considerations/constraints, and use of prior data. Ideally, automated training of machine intuition will enable more agile manufacturing, high-throughput screening of materials and geometries, and more efficient collaboration across systems. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Additive Manufacturing, Mechanical Properties |