Additive Manufacturing of Ceramic-based Materials: Process Development, Materials, Process Optimization and Applications: Session III: Laser Powder Bed Fusion and Novel AM Processes
Sponsored by: ACerS Engineering Ceramics Division, ACerS Basic Science Division, ACerS Manufacturing Division
Program Organizers: Xuan Song, University of Iowa; Lei Chen, University of Michigan-Dearborn; Xiangyang Dong, Arizona State University; Yiquan Wu, Alfred University; Paolo Colombo, University of Padova; Rajendra Bordia, Clemson University; Long-Qing Chen, The Pennsylvania State University
Tuesday 2:00 PM
October 19, 2021
Room: A112
Location: Greater Columbus Convention Center
Session Chair: Fei Peng, Clemson University
2:00 PM Invited
Machine-learning-based Microstructure-property Prediction Enabled by High-throughput Ceramic Sample-array Preparation Using Integrated Additive/Subtractive Manufacturing: Xiao Geng1; Jianan Tang1; Dongsheng Li2; Yunfeng Shi3; Rajendra Bordia1; Jianhua Tong1; Xiao Hai1; Fei Peng1; 1Clemson University; 2Advanced Manufacturing LLC; 3Rensselaer Polytechnic Institute
We demonstrate a high throughput sample fabrication method, and a novel machine learning algorithm that can predict the microstructure of laser-sintered alumina. The alumina sample array was prepared using laser-based integrated additive/subtractive manufacturing (IASM) method. We can simultaneously fabricate a sample array that contains hundreds of individual sample units. These sample units have their own microstructure, depending on the laser power distribution and the sample locations. Micro-indentation was carried out to measure the hardness of all the sample units. The microstructure of selected sample units was characterized using SEM. Thus, we efficiently established a database of microstructure and hardness of laser-sintered alumina. We developed a novel machine learning algorithm, regression-based conditional generative adversarial networks (GANs) with Wasserstein loss function and gradient penalty (RCWGAN-GP). We found that RCWGAN-GP can not only accurately regenerate the microstructure of alumina with measured hardness, but also accurately predict the microstructure of alumina of arbitrary hardness.
2:30 PM
Direct-Ink-Writing and Cold-Sintering of ZnO Ceramics: Russell Maier1; Igor Levin1; Lawrence Friedman1; Andrew Allen1; Abhay Goyal1; Nicos Martys1; 1National Institute of Standards and Technology
Efforts are underway at NIST to develop computer modeling tools and standardized experimental methods for characterizing critical steps of direct-ink-writing (DIW) of ceramic materials. Current research focuses on a model system of colloidal ZnO suspensions. We are synthesizing ZnO particles from solution to develop slurries that contain monodispersed and bimodal suspensions of spherical particles to promote green-body packing density and improve the sinterability of printed parts. Concurrently, we are developing computational methods that combine Discrete Element Method (DEM) and multi-phase Computational Fluid Dynamics (CFD) to model DIW printability using the knowledge of particle size/morphology, rheological properties of the slurry, interparticle forces, and physical properties of an extruder. The recently invented cold sintering process permits ZnO densification at temperatures that are an order of magnitude lower than those used in traditional sintering. We are working to understand the physical mechanisms of cold sintering, as required for its effective integration with DIW.
2:50 PM
Additively Manufactured Carbon/Carbon Composites via Direct Ink Writing of Phenolic Resin Precursors: Caitlyn Clarkson1; Connor Wyckoff1; William Costakis1; Matthew Dickerson1; Hilmar Koerner1; 1Air Force Research Laboratory
Additive manufacturing (AM) offers key benefits to manufacturing: freedom of design, reduced material waste, and rapid prototyping, to name a few. However, few AM methods have been investigated for the creation of Carbon/Carbon (C/C) composites, which are well known for having excellent high temperature properties. A direct ink writing (DIW) method, one of many established AM processes, is presented for creating C/C composite structures from liquid phenolic resin inks. A low molecular weight gelator, which self-assembles in solution, was used to modify the ink rheology and carbon fiber content was varied for its impact on the mechanical properties. This work examined the properties of the inks leading to the best prints as well as the properties of the cured composite (carbon-conversion, carbon fiber orientation, and mechanical performance).
3:10 PM
Bulk Ferroelectric Metamaterial with Giant Piezoelectric Coefficients and Biomimetic Mechanical Property from Additive Manufacturing: Jun Li1; 1University of Wisconsin-Madison
Three-dimensional (3D) ferroelectric materials are promising electromechanical building blocks for achieving human-machine interfacing, energy sustainability, and enhanced therapeutics. However, current natural or synthetic materials cannot offer both high piezoelectric responses and desired mechanical toughness at the same time to meet the practicality. Here, a nacre-mimetic ferroelectric metamaterial was created with a ceramic-like piezoelectric property and a bone-like fracture toughness through electric-field-assisted 3D printing. The as-printed bulk structure, consisting of periodically intercalated soft ferroelectric and hard electrode layers, exhibited a giant d33 of over 130 pC/N, as well as a superior fracture resistance of ~ 5.5 MPa·m1/2, more than three times higher than conventional piezo-ceramics. The excellent printability together with the unique combination of both high piezoelectric and mechanical behaviors allowed us to create artificial piezoelectric bones with tunable anisotropic piezoelectricity and bone-comparable mechanical properties, marking a cornerstone toward manufacturing practical, high-performance, and smart biological systems.