About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Generating Novel Porosity Distributions Produced by Metal Additive Manufacturing via Deep Learning |
Author(s) |
Odinakachukwu Francis Ogoke, Chris Laursen, Kyle Johnson, Michael Glinsky, Sharlotte Kramer, Amir Barati Farimani |
On-Site Speaker (Planned) |
Odinakachukwu Francis Ogoke |
Abstract Scope |
AM-produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Therefore, a precise understanding of the porosity distribution is crucial for accurately simulating potential fatigue and failure zones. In this work, a method for generating new, synthetic, samples of porous parts with novel porosity distributions from limited amounts of training data is presented. To do so, the generation problem is deconstructed into its constitutive parts. First, new examples of the individual pore geometries and surface roughness are created using Generative Adversarial Networks and Scattering Transformations, then, these components are sampled to construct new examples of a porous printed part. The generated parts are compared to the existing experimental porosity distributions based on statistical and dimensional metrics, such as nearest neighbor distances, pore volumes, pore anisotropies and scattering transform based auto-correlations. |