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

01/01/2022

Postdoc Qualifications

Candidates are expected to have a background in multiscale modeling of material behavior including crystal plasticity, dislocation density evolution, and microstructure characterization.

Co-Advisors