Model-Centric Certification of Additive Manufacturing: An Enabling Tool for Accelerated Deployment of Additive Manufacturing to Industrial Applications
|Interdisciplinary Areas:||Data and Engineering Applications, Future Manufacturing
This project aims at establishing a comprehensive digital twin (DT) as the real-time digital replica of physical metal additive manufacturing processes. Metal additive manufacturing (AM) offers unique opportunities for new design and manufacturing of many of these components with properties and shapes not possible by any conventional manufacturing processes. However, despite many on-going efforts, key metal AM challenges remain, including: 1) a lack of truly predictive capabilities of parameter-structure-property relationships that can lead to design optimization of AM parts as existing efforts deal with only a portion of these relationships and specific material; 2) a lack of methods to predict part performance, which results in mostly empirical validation of AM parts, and 3) a lack of in-process monitoring capabilities and therefore mostly open-loop AM processes. We seek to address these gaps by developing a comprehensive digital twin as integrated, key enabling technologies for efficient and robust implementation of additive manufacturing. DT integrates technologies such as multiphysics multiscale modeling, smart sensing, and machine learning. We will establish integrated predictive modeling of parameter-structure-property relationships with a representation of spatially distributed properties based on computationally efficient multiscale modeling and data-driven modeling considering relevant microstructural details and the effects of operating parameters.
May 1, 2024
Someone who has done research related to metal additive manufacturing with both the modeling and experimental experiences. Some background on mechanics of material and/or machine learning is a plus.
Yung C. Shin (ME): firstname.lastname@example.org
Vikas Tomar (AAE): email@example.com
• Porro, M., Zhang, B., Parmar, A. and Shin, Y.C., "Data-driven Modeling of Mechanical Properties for 17-4 PH Stainless Steel Built by Additive Manufacturing", Integrating Materials and Manufacturing Innovation, 2022. https://doi.org/10.1007/s40192-022-00261-8
• Elkhateeb, M. and Shin, Y.C., "A Microstructure-Based Extended Mechanics of Structure Genome for the Prediction of the Mechanical Behavior of Additively Manufactured Ti6Al4V Considering Porosity and Microstructure", Mechanics of Materials, Volume 169, June 2022, 104300. https://doi.org/10.1016/j.mechmat.2022.104300
• Elkhateeb, M., Liu, S. and Shin, Y.C., "Analysis of the Effects of Microstructure Heterogeneity on the Mechanical Behavior of Additively Manufactured Ti6Al4V Using Mechanics of Structure Genome", Materials and Design, 2021, Volume 204, June 2021, 109643.
• Shin, Y.C., Bailey, N., Katinas, C., Tan, W., "Predictive Modeling Capabilities from Incident Powder and Laser to Mechanical Properties for Laser Directed Energy Deposition", Computational Mechanics, May 2018, Volume 61, Issue 5, pp 617-636.