image: Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty, Khodabakhshi was a postdoctoral fellow at the Oden Institute for Computational Engineering and Sciences. She holds bachelor’s and master’s degrees in civil engineering from Sharif University of Technology and earned her PhD in civil engineering from Texas A&M University. Her research areas include computational mechanics, nonlocal mechanics, scientific machine learning, data-driven model reduction, and multifidelity methods.
Credit: Lehigh University
Additive manufacturing, commonly referred to as 3D printing, is a manufacturing technology that builds objects layer by layer using materials such as metals, polymers, or biomaterials.
“This layer-by-layer approach allows for the fabrication of parts with complex geometries that are often difficult, or even impossible, to achieve with conventional manufacturing methods,” says Parisa Khodabakhshi, an assistant professor of Mechanical Engineering and Mechanics at Lehigh University’s P.C. Rossin College of Engineering and Applied Science. “However, the thermomechanical properties of the final additively manufactured parts are influenced by a large number of process parameters, making design optimization particularly challenging.”
Establishing the map between variations in process parameters and the final part’s properties requires several simulations across a wide range of length scales, making the task computationally expensive. “The computational demands of performing all the necessary simulations make it impractical,” says Khodabakhshi. As a result, manufacturers often resort to trial-and-error methods to achieve desired thermal or mechanical properties in the end product. “However, you cannot fully explore the entire design space that way to find the optimal design, which is why we’re currently not able to utilize the full potential of additive manufacturing.”
Khodabakhshi recently received a three-year, $350,000 grant from the National Science Foundation to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure, which in turn determines the properties of the final part. Specifically, Khodabakhshi will develop a physics-based, data-driven reduced-order model for predicting microstructure evolution in binary alloy solidification (or when a mixture of two metals changes from liquid to solid).
“For example, say I want a part that has specific thermal properties,” she says. “I don’t know what my process parameters should be to achieve those properties. The simulations that link given process parameters to the resulting solidification microstructure, and consequently the final properties of the built part, are highly nonlinear. We refer to this simulation as the forward map. From there, I can construct the inverse map, which connects desired properties back to the process parameters.” The NSF project focuses on developing a computationally efficient model for the process-structure (PS) relationship.
The ultimate goal is to optimize the manufacturing of additively manufactured parts, which are especially useful in the aerospace, automotive, and healthcare industries. Fields in which confidence in manufacturing is paramount.
Her team’s approach uses a scientific machine learning framework that blends data-driven machine learning algorithms with physical laws. “As engineers, we don’t want to just train a black-box algorithm,” says Khodabakhshi. “We want to embed physics into the problem to satisfy the governing equations of the physical phenomena so that we’re confident about the output that we receive from the algorithm. That’s the difference between conventional machine learning and scientific machine learning.”
About Parisa Khodabakhshi
Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty, Khodabakhshi was a postdoctoral fellow at the Oden Institute for Computational Engineering and Sciences. She holds bachelor’s and master’s degrees in civil engineering from Sharif University of Technology and earned her PhD in civil engineering from Texas A&M University. Her research areas include computational mechanics, nonlocal mechanics, scientific machine learning, data-driven model reduction, and multifidelity methods.
Related Links
- Rossin College Faculty Profile: Parisa Khodabakhshi
- NSF Award Abstract (2450804): CDS&E: Development of Data-Driven Physics-Based Reduced-Order Models for the Solidification Process of Binary Alloys
- Lehigh University: Institute for Data, Intelligent Systems, and Computation (I-DISC)