Additive Manufacturing: Mechanical Behavior of Lattice Structures Produced via AM: Additive Manufacturing of Lattices - Session I
Sponsored by: TMS: Additive Manufacturing Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: John Carpenter, Los Alamos National Laboratory; Matthew Begley, University of California, Santa Barbara; Sneha Prabha Narra, Carnegie Mellon University; Michael Groeber, Ohio State University; Isabella Van Rooyen, Pacific Northwest National Laboratory; Kyle Johnson, Sandia National Laboratories; Krishna Muralidharan, University of Arizona
Monday 8:00 AM
November 2, 2020
Room: Virtual Meeting Room 4
Location: MS&T Virtual
Session Chair: John Carpenter, Los Alamos National Laboratory; Krishna Muralidharan, University of Arizona
8:00 AM Invited
Process-Aware Design of Additively Manufactured Lattice Structures: Carolyn Seepersad1; Conner Sharpe1; 1University of Texas at Austin
Additively manufactured lattice structures are well-suited for lightweight structural and other multifunctional applications. However, the additive manufacturing process can contribute to various defects in the structures, which can lead to diminished mechanical properties. The small struts that comprise architected materials exacerbate the process-induced variability in mechanical properties, which often takes the form of size- and orientation-dependent strut-level material properties that differ from those of the bulk material. In this research effort, the extent of these strut- and lattice-level property variations are characterized experimentally, and the findings serve as input to process-aware techniques for designing additively manufactured lattice structures with improved properties that are more predictable.
8:30 AM Invited
High-Throughput Screening of Additive Lattices using a Deep Neural Network: Brad Boyce1; Anthony Garland1; Benjamin White1; Bradley Jared1; Michael Heiden1; Emily Donahue1; 1Sandia National Laboratories
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.)
9:00 AM Invited
Mesoscale Open Structures for Lightweight Structures: Joseph Newkirk1; K. Chandrashekhara1; 1Missouri University of Science and Technology
Metallic materials have desirable properties for transportation systems due to their damage tolerance, fabricability, and stiffness. However, metals by character are relatively dense. High strength and high stiffness are necessary to acceptable specific strength and stiffness values. At the macro scale, truss and other engineering structures produce lowered densities. At the microscale, metal matrix syntactic foams offer improved specific properties but suffer from significant fabrication issues. Mesoscale structures, sometimes called lattice or cellular structures, offer improved properties and can be fabricated using AM. In this talk the issues with producing high performance, reliable structures will be discussed with examples. The combination of a defect producing process such as AM and a defect sensitive structure will require significant understanding of this interaction and strategies for mitigating them.
9:30 AM Invited
Effect of Processing on Micro/Mesoscale Structures and Properties of Stainless Steel 316L Lattices: Allison Beese1; Cole Britt1; 1Pennsylvania State University
Laser powder bed fusion (PBF) additive manufacturing (AM) can be used to fabricate intricate structures, including lattice structures that are promising for lightweight high stiffness applications. However, when fabricating struts on the order of 1 mm or smaller in diameter with PBF, the effects of surface roughness, upskin and downskin layers, and bulk processing parameters may all play a role in the ultimate mechanical performance of the lattices. This study worked to determine the interrelationships between strut thickness, surface roughness, grain size/morphology, texture, sub-grain cell size, and microhardness. Namely, the microstructural features and microhardness of stainless steel 316L lattice structures fabricated through laser powder bed fusion, using a range of processing parameters, were characterized to identify these relationships.
10:00 AM
Additive Manufacturing Laser Powder Bed Fusion Optimization for Dissolvable Supports with SS 316L: Shawn Hinnebusch1; Kevin Glunt1; Robert Hoffman2; Owen Hildreth2; Albert To1; 1University of Pittsburgh; 2Colorado School of Mines
Creating parts by laser powder bed fusion (LPBF) process can be challenging as support material is usually required for complex parts. As LPBF can only use one material, creating a dissolvable support structure has many challenges. To have a dissolvable support, a low-density structure is usually required, but this type of structure typically has cracking and high distortion because of the residual stress. By using topology optimization, the material can be minimized while still meeting the criteria for residual stress. Using a self-terminating solution, the part will retain the material while the support structure can be fully dissolved. The goal of this project is to develop a structure/design that can ensure printability while maintaining fluid flow across all the support to allow the structure to be fully dissolvable.