Machine Learning and Artificial Intelligence Enabled Rapid Nanoscale 3D Printing

Interdisciplinary Areas: Future Manufacturing

Project Description

3D printing is one of the most important technology developments in recent history for applications ranging from prototyping, product visualization, to building functional components and devices. Nanoscale 3D printing based on femtosecond laser two-photon polymerization has been used to fabricate a wide range of nanostructured materials and devices with unprecedented properties and functionalities. However, the slow speed of 3D nanoprinting, which is a point-by-point printing process, is a major obstacle to its adoption for a wider spread of applications. Recently, a rapid, continuous, layer-by-layer femtosecond laser projection 3D nanoprinting technology has been developed at Purdue. This project is to incorporate machine learning (ML) and artificial intelligence (AI) in 3D nanoprinting to improve the robustness of the 3D printing process and the 3D printing accuracy. Various advanced ML algorithms, in combination with physics-based modeling, will be used to develop ML and AI tools for printing arbitrary nanoscale 3D geometries. Femtosecond laser processing and photo-polymerization processes will also be investigated. The ultimate goal is to develop ML and AI-enabled rapid and robust 3D nanoprinting technology to produce precise 3D parts with a significantly higher printing speed compared with state-of-the-art 3D nanoprinting systems.

Start Date


Postdoc Qualifications

Ph.D. in, Mechanical Engineering, Mathematics, Computer Science, Physics, or relevant background in femtosecond laser-based manufacturing and/or machine learning.


Xianfan Xu,, James J. and Carol L. Shuttleworth Professor of Mechanical Engineering,
Guang Lin,, Professor, Department of Mathematics (primary appointment), Professor, Department and Statistics, Professor, School of Mechanical Engineering,
*Somers, P., Boudouris, B., Pan, L., and Xu, X., Rapid 3D nanoprinting utilizing spatiotemporal focusing, SPIE Photonics West, San Francisco, CA, March 2021, also under review for publication.
*Barton, P., Mukherjee, S., Prabha, J., Boudouris, B.W., Pan, L., and Xu, X., 2017, Fabrication of silver nanostructures using femtosecond laser-induced photoreduction, Nanotechnology 28, 505302.
*Chi, T., Somers, P., Wilcox, D.A., Schuman, A.J., Johnson, J.E., Liang, Z., Pan, L., Xu, X., and Boudouris, B.W., 2021, Substituted Thioxanthone-Based Photoinitiators for Efficient Two-Photon Direct Laser Writing Polymerization with Two-Color Resolution, ACS Appl. Polym. Mater. 3, 3, 1426-1435.
*Chen, J., Kang, L., Lin, G., 2020, Gaussian process assisted active learning of physical laws, Technometrics, DOI: 10.1080/00401706.2020.1817790.
*Zhang, S., Lin, G., 2018, Robust data-driven discovery of governing physical laws with error bars, Proceedings of the Royal Society of London. Series A, mathematical, physical and engineering sciences, DOI: 10.1098/rspa.2018.0305