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Meeting Materials Science & Technology 2020
Symposium Additive Manufacturing: Mechanical Behavior of Lattice Structures Produced via AM
Presentation Title High-Throughput Screening of Additive Lattices using a Deep Neural Network
Author(s) Brad L. Boyce, Anthony Garland, Benjamin White, Bradley Howell Jared, Michael Heiden, Emily Donahue
On-Site Speaker (Planned) Brad L. Boyce
Abstract Scope Additively manufactured metamaterials such as lattices offer unique physical properties such as high specific strengths and stiffnesses. However, additively manufactured parts, including lattices, exhibit a higher variability in their mechanical properties than wrought materials, placing more stringent demands on inspection, part quality verification, and product qualification. Previous research on anomaly detection has primarily focused on using in-situ monitoring of the additive manufacturing process or post-process (ex-situ) x-ray computed tomography. In this work, we show that convolutional neural networks (CNN), a machine learning algorithm, can directly predict the energy required to compressively deform gyroid and octet truss metamaterials using only optical images. Using the tiled nature of engineered lattices, the relatively small data set (43 to 48 lattices) can be augmented by systematically subdividing the original image into many smaller sub-images. During testing of the CNN, the prediction from these sub-images can be combined using an ensemble-like technique to predict the deformation work of the entire lattice. This approach provides a fast and inexpensive screening tool for predicting properties of 3D printed lattices. Importantly, this artificial intelligence strategy goes beyond ‘inspection’, since it accurately estimates product performance metrics, not just the existence of defects. (Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.)
Proceedings Inclusion? Planned: At-meeting proceedings


Additive Manufacturing Laser Powder Bed Fusion Optimization for Dissolvable Supports with SS 316L
Direct Metal Laser Sintering Strategies for Fabrication of Finer Resolution Cellular Structures
Effect of Processing on Micro/Mesoscale Structures and Properties of Stainless Steel 316L Lattices
High-Throughput Screening of Additive Lattices using a Deep Neural Network
Mechanical Properties of Additively Manufactured Metal Lattices
Mesoscale Open Structures for Lightweight Structures
Microstructure and Mechanical Properties of Additively Manufactured Lattice Structures of Co-Cr-Mo Alloy
Predicting Interfacial Cracking between Solid and Lattice Support Structure during Laser Powder Bed Fusion Processing
Predicting the Response of Additively Manufactured IN625 Thin-walled Elements
Process-Aware Design of Additively Manufactured Lattice Structures
Residual Stress Mitigation in Lattice Structures Built by Laser Powder Bed Fusion
Tailoring Hierarchical Material Performance Through Process Manipulation

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