The goal of this team is to create educational tools for nanoscale additive manufacturing (AM) based on two-photon absorption (TPA) so that high-skills nanoscale three-dimensional (3D) printing can become as prolific as hobbyist 3D printing is today. TPA-based nanoscale AM has the unique ability to use light to produce 3D structures with features as small as 100 nm. These structures have demonstrated significant promise in improving healthcare, transportation, and computing. However, the field of nanoscale AM remains inaccessible to most of the K-12 and college-level students due to the prohibitively expensive equipment and the need for extensive hands-on training in process planning. Our goal here is to eliminate these barriers by creating a low-cost nanoscale 3D printer and a mobile app-based process simulator.
Our first goal is to design, build, and test a low-cost nanoscale 3D printer. Cost reduction will be achieved by replacing the expensive ultrafast laser used in commercial nanoscale 3D printers with low-cost light sources. A key research topic is to investigate and overcome the tradeoff between the cost of the light source and the resolution of printing. Our target is to achieve a printing resolution of at least 500 nm with a printer that costs no more than $10,000.
Our second goal is to design, build, and test process simulators for nanoscale 3D printing based on computationally efficient machine learning (ML) models. The ML models will be trained using data generated from experiments and physics-based computational models of printing. A key research topic is to investigate and overcome the tradeoff between the accuracy of prediction and the computational cost of the simulators. Our target is to achieve a prediction accuracy better than +/- 5% with a smartphone device.
Issues Involved or Addressed
(1) Designing and building opto-mechanical systems (2) Formulating feedstock materials and photoresists (3) 3D printing and nanoscale characterization of the printed structures (4) Modeling of photopolymerization via reaction-diffusion mechanisms (5) Modeling of light propagation through an optical system (6) Generating machine learning models for image processing (7) Generating machine learning models for regression analysis
Methods and Technologies
Academic Majors of Interest
- Computing›Computational Science and Engineering
- Computing›Computer Science
- Engineering›Chemical and Biomolecular Engineering
- Engineering›Electrical Engineering
- Engineering›Machine Learning
- Engineering›Materials Science and Engineering
- Engineering›Mechanical Engineering
Preferred Interests and Preparation
ME, MSE, EE, ChBE: Background/interest in 3D printing. Familiarity with CAD and prior experience in designing and building mechanical systems is encouraged but not required. Background/interest in machine learning, finite element simulations, or Fourier optics techniques is encouraged but not required. CS, Applied Math, Stats, CSE – Background/interest in machine learning, data-driven methods, or finite element techniques. Interest in the intersection of machine learning and engineering. Curiosity about 3D printing. Programming skills encouraged but not required.
Meeting Schedule & Location
- Mechanical Engineering