Project Description

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.

Start Date

08/01/2023

Postdoc Qualifications

The candidate must have worked on some elements of modeling dealing with metal additive manufacturing. The modeling experiences can include thermal, microstructure or mechanical property predictions. Some background on data-driven modeling is useful, but not required. 

Co-Advisors

Yung C. Shin (ME) and Vikas Tomar (AAE) 

Bibliography

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.

Bailey, N. and Shin, Y.C., "Multi-track, Multi-layer Dendrite Growth and Solid Phase Transformation Analysis during Additive Manufacturing of H13 Using a Hybrid Cellular Automata/Phase Field model", International Journal of Advanced Manufacturing Technology, 120, pages 2089-2108 (2022).

Liu, S. and Shin, Y.C., "A Novel 3D Cellular Automata-Phase Field Model for Computationally Efficient Prediction of Dendrite Formation during Solidification in a Large Domain", Computational Material Science, Volume 192, May 2021, 110405.

Katinas, C. and Shin, Y.C., "Prediction of Initial Transient Behavior with stationary heating during Laser Powder Bed Fusion Processes", International Journal of Heat and Mass Transfer, Volume 153, June 2020, 119663

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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.