The maturation of Additive Manufacturing into a viable industrialization technology for wide-scale production requires an expanded notion of integration, encompassing AM equipment, data, and simulation tools. To facilitate this, this opportunity focuses on building the necessary data and computation infrastructure to enable the Integration of Computational Materials Engineering (ICME) in an AI platform for industrial AM production.

This research philosophy relies on a dual approach:

• Data Informatics & Analytics: Leading investigations into the root causes of data variability to improve product quality and reproducibility [1].
  
  
• Simulation Modeling: Developing theoretical and mathematical descriptions of physical phenomena, including both physics-based and data-driven models [2, 3].
  
  
• Metric Identification: Identifying key quality metrics for various "digital objects" throughout the ICME development lifecycle.
• Uncertainty Quantification & Propagation: Developing computational methods to propagate quality issues, such as uncertainty, from upstream data sources to downstream digital objects. This propagation is crucial for ensuring the overall trustworthiness of the ICME workflow.

Currently, the Data Informatics and Management project at the NIST Engineering Laboratory is pivotal in investigating key information required for part qualification and certification in AM processes. The outcome is a scalable digital workflow designed to streamline technology transfer from low to high readiness levels, promoting industry-wide adoption of standardized data practices. A wide range of experience with computational materials, software development, and/or additive manufacturing is welcome.

[1] Toward a Standard Data Architecture for Additive Manufacturing, Li, S., Feng, S., Kuan, A., & Lu, Y. (2024). JOM, 76(4), 1905-1912.

[2] Revisiting alloy design of low-modulus biomedical β-Ti alloys using an artificial neural network, Wu, C. T., Lin, P. H., Huang, S. Y., Tseng, Y. J., Chang, H. T., Li, S. Y., & Yen, H. W. (2022). Materialia, 21, 101313.

[3] Dynamic plasticity model for rapidly heated 1045 steel up to 1000 C, Mates, S. P., & Li, S. Y. (2021). Journal of Research of the National Institute of Standards and Technology, 126, 126026.

Computational Materials for Manufacturing; Artificial Intelligence; Uncertainty Quantification; Verification and Validation; Additive Manufacturing

citizenship

Open to U.S. citizens

level

Open to Postdoctoral applicants