Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process: Poster Session
Program Organizers: Jing Zhang, Purdue University in Indianapolis; Brandon McWilliams, US Army Research Laboratory; Li Ma, Johns Hopkins University Applied Physics Laboratory; Yeon-Gil Jung, Korea Institute of Ceramic Engineering & Technology
Monday 5:00 PM
October 10, 2022
Room: Ballroom BC
Location: David L. Lawrence Convention Center
Session Chair: Yeon-Gil Jung, Changwon National University; Li Ma, Johns Hopkins University Applied Physics Laboratory; Brandon McWilliams, CCDC Army Research Laboratory; Jing Zhang, Indiana University - Purdue University Indianapolis
A-2: A Data-driven Approach to Identify Structural Characteristics that Connect Macroscale Material Properties: Matthew Beck1; Mir Al-Masud1; Ryan Griffith1; Naji Mashrafi1; 1University of Kentucky
In the study and modeling of new materials, structural complexity has long been a challenge. Technology has advanced to the point that computational techniques have been developed to generate simulation data that is automated and accelerated. Microstructure, with several functional modes that are connected in their operation, contributes to the overall property of a material at a macroscale level. This study aims to implement a data-driven and automated process that allows us to identify structural characteristics that have the largest impact on macroscale material properties. Using a multiple step workflow including data pre-processing, microstructure quantification, dimensionality reduction, and validation of the relationship between macro and microscale quantities we have deduced connection between microstructure and properties of randomly structured porous and solid materials.
A-3: Corrosion and Mechanical Properties of Additively Manufactured 316L Stainless Steel Coated with Epoxy: Xuehui Yang1; Francisco Rodriguez1; Hyun-Hee Choi2; Yeon-Gil Jung2; Alan Jones1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis; 2Changwon National University
Additively manufactured (AM’ed) alloys have been increasingly used for medical and structural applications. However, their corrosion properties are not sufficiently studied which hinders their extensive applications. In this work, the electrochemical and mechanical properties of AM’ed 316L stainless steel were studied. A commercial epoxy layer was applied to the steel for improved corrosion resistance. The interfacial bond strength was experimentally measured and simulated with the molecular dynamics method to explore the adhesion properties of the coating layer. Both experimental and computational results provide insights for the AM alloys used in corrosive environments.
A-4: Design and Mechanical Properties of 3D Printed Bioinspired Honeycomb Structures: Francisco Rodriguez1; Amir Abbas Yahyaeian1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
Bioinspired honeycomb structures are the light weighted while strong structural objects. In this work, multiple ABS honeycomb patterns and structures were designed, and 3D printed. The designs consisted of different wall thicknesses, braces around the edge of the structure, or having a plate at the top or bottom for support. Additionally, the mechanical properties of the fabricated honeycomb structures were simulated using a finite element software package. The correlation between strength and weight was determined. With this design, one can now determine the amount of material that is needed based on how much load the structure will need to withstand. This leads to a major improvement in efficiency for the industry.
A-5: Development a Customized Inkjet 3D Printer for Ceramic Component Fabrication: Haoyee Yeong1; Zhen Hong Tan1; Aizat Zazlan1; Ben Louie Yap1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
Inkjet 3D printing utilizes layer-by-layer droplet interaction and solidification to generate 3D components. In this work, using an open-source design, we created and modified the customized 3D inkjet printer to improve its printability. The preliminary results of the design and fabrication of the printer system are presented and experimentation with the inkjet printer head is performed. The printer’s performance was evaluated, and the printed components’ mechanical properties were measured. The project illustrates the effectiveness of this promising manufacturing technique for ceramics fabrications.
A-6: Fabrication and Characterizations of 3D Printed Lithium-Ion Battery Electrodes: Eli Kindomba1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
In this work, additive manufacturing (AM) or 3D printing techniques are employed to fabricate lithium-ion battery (LIB) electrodes through the fused deposition modeling (FDM) method. In this study, we investigate the formulation, printability, and electrochemical characterization of anode and cathode filament based on a formulation process modified from the literature. With a composite of graphite/polylactic acid (PLA) serving as the anode and two composites of Lithium cobalt (III) oxide (LCO)/PLA and Lithium nickel manganese cobalt oxide (NMC)/PLA serving as cathodes, various filaments are fabricated and tested. Using varying material ratios, those filaments are studied and compared based on their printability, mechanical characteristics, and electrochemical performances. Through this study, 3D printed electrodes and separator can be assembled to form 3D printed lithium-ion batteries of complex desired shapes with optimized energy density and sufficient mechanical strength.
A-7: Layerwise Thermal Process Simulation for Laser Powder Bed Fusion: Calibration and Validation with Infrared Camera: Shawn Hinnebusch1; Alaa Olleak1; Christopher Barrett2; Seth Strayer1; Florian Dugast1; Albert To1; 1University of Pittsburgh; 2Open Additive, LLC
Current part-scale thermal process simulation models for laser powder bed fusion (L-PBF) are calibrated using thermocouple data and do not capture the correct temperature distribution. Most layerwise simulations apply a constant heat flux for the duration of the layer or apply the melting temperature to the layer for a set time. These methods are tuned with the absorptivity and heat convection coefficients for a set geometry to match experimental data. With the help of thermocouples and an infrared (IR) camera, the simulations can be accurately calibrated for various geometries by tuning the model parameters throughout the build process. This proposed workflow uses a voxel mesh and matrix-free formulation to take full advantage of GPU-based computing allowing for rapid and accurate calibration of L-PBF process simulation models.
A-8: Modeling of Fatigue Behavior of 3D Printed Polycrystal Metals: Sanket Kulkarni1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
Fatigue properties are critical for structures subject to cyclic load. In this work, polycrystal metal microstructure models were created using software, then the structures were exported to a finite element software package, with all the required properties embedded in the pipeline. The finite element software was used to conduct a fatigue analysis, based on the microstructure, to predict the mechanical behavior of 3D metal printed materials.
A-9: Reducing the Order of a Kinetic Monte Carlo Potts Solidification Model with Machine Learning: Gregory Wong1; Anthony Rollett1; Gregory Rohrer1; 1Carnegie Mellon University
Current microstructure and grain orientation models are expensive to run, and this work seeks to reduce their order using machine learning. Categorical generative adversarial networks (cGAN) and variational autoencoders (VAE) are examined as methods to reduce the order of a Monte Carlo Potts Solidification model. This work is a proof of concept for use with larger models for metal additive manufacturing. A cGAN is a network of convolutional neural networks (CNNs) that can generate an image that the computer has classified as being from a specific labeled group. A VAE, another network of CNNs, correlates image statistics with labels to produce images with specific labels. Both models have been trained to produce images with varied nuclei count. These methods will allow for printing parameters to be fed into the model to create a specific microstructure in future work. Training and output images along with model structure will be presented.
A-10: Smoothed Particle Hydrodynamics Modeling of Charpy Impact Test of A36 Steel: Sugrim Sagar1; Amir Abbas Yahyaeian1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
In this study, a Smoothed Particle Hydrodynamics (SPH) numerical model was developed to simulate the Charpy impact test of A36 steel. The Johnson-Cook constitutive and damage material models were used for the simulation. The simulation results showed that an increase in temperature resulted in a reduction in the Charpy impact energy. The developed SPH model results are in good agreement with experimental observations.
A-11: Thermal Barrier Coating with Additively Manufactured Nickel Base Superalloy Substrate: Tejesh Dube1; Junseong Kim2; Yeon-Gil Jung2; Jing Zhang1; 1Indiana University – Purdue University Indianapolis; 2Changwon National University
In this work, a new thermal barrier coating (TBC) deposited on additively manufactured (AM) nickel base superalloy substrate was developed. Yttria stabilized zirconia (YSZ), NiCrAlY and nickel base superalloy were used as the topcoat, bond coat, and substrate respectively. Selective laser melting (SLM) was used to fabricate buttons of 718 alloy for the TBC system with AM substrates. Microstructure and phase analyses were carried out to understand the effect of the fabrication route on the microstructure of the substrate. Mechanical property and thermal durability tests were also conducted.
A-12: Utilizing Virtual Reality to Help Educate Additive Manufacturing: Josh Hale1; Shambhuraj Wadghule1; Jing Zhang1; 1Indiana University – Purdue University Indianapolis
Additive Manufacturing (AM) is a manufacturing method of creating a three-dimensional part by layer-by-layer stacking and fusing materials. In this work, we present a virtual reality (VR) module with the aim of educating users about the common features of 3D printing machines, in order to improve the learning experience for students. The module is capable to simulate metal printing technology and explore the key phenomena in the process.