Additive Manufacturing: Large-Scale Metal Additive Manufacturing: On-Demand Oral Presentations
Program Organizers: Yousub Lee, Oak Ridge National Laboratory; Antonio Ramirez, Ohio State University; Yashwanth Bandari, 'Meltio Inc.; Duckbong Kim, Tennessee Technological University; Wei Zhang, Ohio State University
Friday 8:00 AM
October 22, 2021
Room: On-Demand Room 1
Location: MS&T On Demand
Moving Heat Source Process Simulation for Wire Arc Additive Manufacturing via a Mesh-free Method and GPU Computing: Xavier Jimenez1; Florian Dugast1; Alaa Olleak1; Albert To1; 1University of Pittsburgh
Wire arc additive manufacturing (WAAM) combines a high deposition rate and the ability to easily increase build volume to produce large-scale metal parts. The high deposition rate is also tied into high energy inputs, so thermal management becomes important to ensure the properties of the material remain the same throughout the entire part not only in the build direction but also along the build plane. Due to the large computation time, many CPU finite element method (FEM) tools for large-scale parts use a layer-by-layer one-shot activation approach. This talk presents a mesh-free GPU moving heat source process simulation model for WAAM that can accurately determine the effects of the printing parameters without compromising simulation time. The results showed good accuracy when compared to a FEM model while obtaining a speedup of 10x. Several simulation examples will be demonstrated on WAAM parts to show its efficiency and variation of melt pool.
A Proposed Sustainable Framework to Assess Wire Arc Additive Manufacturing Efficiency in Processing of Different Mechanical Components: Mohamed Fawzy Mohamed1; Ahmed Salem1; Ahmed Elsokaty1; Hanadi Salem1; 1The American University in Cairo
In this paper, a proposed multilayer-sustainable framework is developed to illustrate the influence of using the Wire Arc Additive Manufacturing (WAAM) technology as a manufacturing process, this toolbox is enhancing the decision making of using the WAAM technology instead of using other conventional or advanced processing techniques to manufacture mechanical components. The assessment approach is based on capturing the relative manufacturing parameters of different processes applied for selected mechanical components. Additionally, using the most common benchmarking and selective assessment techniques such as Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP), while considering all sustainability Triple bottom-line approach concerned with main economic, environmental, and societal factors in order to support an evidence for using WAAM as a manufacturing process under same conditions and parameters.
Thermo-mechanical FEM Modeling and Machine Learning of Distortion on Overhang Structure in Laser Powder Bed Fusion Additive Manufacturing: Xuesong Gao1; Tyler High1; Jesse Zhu2; Wei Zhang1; Hyeyun Song3; 1The Ohio State University; 2Cornell University; 3Edison Welding Institute
Net or near-net shape parts produced by additive manufacturing typically possess complex structures, e.g., overhang which is disposed to distortion problems. To address this, two kinds of thermo-mechanical models were developed. The first one used a lumped layer method where multiple layers were treated as a single “building block”. The distortion formation was calculated on the full scale part and the model exhibited high accuracy and efficiency. Another model was based on a moving heat source method where each pass and layer was simulated directly. The model geometry was scaled down by 10 times and most processing parameters were accounted for, such as power and scanning speed, etc. To validate these models, overhang structures were printed with in-situ temperature and distortion measurements. A machine learning program was developed to correlate the measured temperature distribution to the measured distortion. The roles of thermal stress on distortion formation were revealed.