Development of a Digital Twin Framework for Metal Additive Manufacturing
This grant seeks to develop a digital twin framework for metal additive manufacturing that integrates AI/ML with physics-based models to improve part quality and consistency.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | August 1, 2025
Funding Amounts: $82,764 stipend plus $3,000 travel allowance; typical appointment duration 2 years.
Summary: Postdoctoral fellowship to develop a digital twin framework integrating AI/ML and physics-based models for metal additive manufacturing to improve part quality and consistency.
Key Information: Open to U.S. citizens holding a doctoral degree; applications require prior contact with research advisers; NIST participates only in February and August review cycles.
Description
This fellowship opportunity supports the development of a digital twin (DT) framework for metal additive manufacturing (AM), focusing on integrating materials-based surrogate models with rigorous physics-based models. The goal is to address current challenges in AM, such as inconsistent quality and properties of metal parts produced by processes like directed energy deposition (DED) and laser powder bed fusion (L-PBF). Existing AI/ML approaches often assume direct correlations between input parameters and output properties but lack incorporation of detailed material characterization and uncertainty quantification (UQ), which are critical for credible digital twin frameworks.
The project aims to create an AI/UQ-enabled DT framework that provides a detailed understanding of structure/property relationships, enabling optimization of the AM process to produce high-quality, repeatable parts. This research is conducted at the National Institute of Standards and Technology (NIST) within the Material Measurement Laboratory, Materials Science and Engineering Division.
Key technical areas include digital twins, additive manufacturing, surrogate modeling, AI/ML, uncertainty quantification, and metal AM processes such as DED and L-PBF.
