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Research Interests
Our lab specializes in Multi-physics-based Advanced Manufacturing, aiming to enhance next-generation manufacturing infrastructures by integrating fundamental physics, data science, and artificial intelligence.
Examples of our key past and ongoing research include:
Physics-informed reduced-order and surrogate models for air-coupled vibration systems.
A surrogate model for drill-pipe resonant fatigue test benches.
Vibration- , vision-, and AI- based in-line monitoring and nondestructive testing methods.
We are seeking highly self-motivated graduate and undergraduate students, as well as postdoctoral researchers, with a keen interest in image processing, vibrations, solid mechanics, and/ or heat transfer on their applications in advanced manufacturing, smart pharmacy packaging, mechanical design, and/ or metrology. Please contact Dr. Feng by email, attaching your CV, all transcripts, standardized test scores (e.g. GRE, TOEFL, or IELTS, etc.), and clearly specify your interest in particular projects or areas. Experience in linear algebra, partial differential equations, numerical analysis, and/ or coding software is preferred.
Mar. 23, 2026:ย
Dr. Feng has been elected as a member of the ASME Technical Committee on Vibration and Sound for the 2026-2029 term. The lab looks forward to contributing further to the vibration community!
Mar. 18, 2026:ย
Boliang Meng, a Ph.D. student at Texas A&M University (chaired by Dr. Pagilla, with Dr. Feng on his committee), has successfully defended his dissertation. In addition, our paper on testing woody chicken breast has been officially published, congratulations to Ziyuan and Charles on this great achievement!๐
Dec. 16, 2025:
Congratulations to Eric Xie, a high school scholar in our lab, on his early action admission to the Massachusetts Institute of Technology! ๐
Nov. 21, 2025:
Weโre thrilled to announce the second journal publication by students from our lab has been published in the Journal of Manufacturing Science and Engineering! ๐
This work is a collaboration with Dr. Ming-Chyuan Lu from National Chung Hsing University and Dr. Jyhwen Wang from Texas A&M University.
In this work, we introduce two in-line monitoring methods for detecting the balling phenomenon in Directed Energy Deposition (DED) processes: the contact angle (CA) method with weighted linear regression and the weighted mask (WM) method with Hadamard product developed by nonlinear regression. Both approaches are well-suited for real-time, in-line feedback control in DED additive manufacturing.
Congratulations to Yangyuan! ๐
And with this milestone, weโre proud to debut our new lab slogan: โWe check balling!โ ๐ชฉ
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