Binil Starly

Adjunct Professor


Binil Starly joined North Carolina State University in August 2013. He directs the Data Intensive Manufacturing Laboratory (DIME Lab). His laboratory is working on technologies that merge the digital and the physical world towards advancing both discrete and continuous manufacturing processes. His specific technical expertise is in digital design and fabrication, cyber-physical systems in manufacturing, additive manufacturing and biofabrication processes. As part of the advanced manufacturing cluster, his work involves digital design tools through machine learning, manufacturing-as-a-service marketplaces and intelligent machines to achieve the goal of democratizing access to design tools and manufacturing services for on-demand highly personalized part production. Such products include personalized products, medical implants, one-off spare parts, etc. Additional information about Starly’s work, including publications and research, can be found at

Starly has received the National Science Foundation CAREER award for research in multi-scale biological tissue scaffold systems built from additive manufacturing platforms. He has published over 45 journal publications in the field of design/manufacturing, customized biomedical implants, biofabrication, and tissue engineering. For his contributions, he has been awarded the 2011 Society of Manufacturing Engineering Young Manufacturing Engineer Award and 2010 NASA TechBrief Award. He has supervised the research of 22 M.S. and 6 Ph.D. students. He is also the advisor of 7 graduate students and 3 undergraduate students. He teaches undergraduate and graduate courses related to Product Development, Digital Design and Manufacturing, Additive Manufacturing and Smart Manufacturing. Starly’s formal education began with a B.S. in Mechanical Engineering from the University of Kerala, India and then a Ph.D. degree in Mechanical Engineering from Drexel University. He then joined the University of Oklahoma to develop additive manufacturing platforms for tissue engineering.



  • Product Development (Undergraduate)
  • Digital Manufacturing (Undergraduate & Graduate)
  • Python for Industrial Engineers with Numerical & Scientific Computing (Undergraduate & Graduate)
  • Smart Manufacturing (Graduate)


NC State Wolfware Outreach
Starly offers the following short online courses that can be taken by any student from around the world. You do not need to be registered on any degree program. Register to gain instant access:

  • “Smart Manufacturing: Moving Machine Data to Cloud via MQTT Protocol” – a 6 week, online only, self-paced course through NC State Wolfware Outreach.
  • “Python Programming Essentials” – a short, online only, self-paced course through NC State Wolfware Outreach.


Research Interests

Binil Starly’s research interests include Digital Manufacturing, Digital Factories, Product Manufacturing Information, Additive Manufacturing, Tissue Biofabrication, Biometrology.



Ph.D.Doctorate of Philosophy in Mechanical EngineeringDrexel University2006
BTechBachelor of Mechanical EngineeringUniversity of Kerala2001

Honors and Awards

  • 2021 | SME Journal of Manufacturing Systems Reviewer of the Year Award
  • 2021 | ISE Outstanding Research Award
  • 2021 | Outstanding Teaching Award, NC State University
  • 2020 | 20 Most-Influential Professor in Smart Manufacturing, Society of Manufacturing Engineers
  • 2018 | C. A. Anderson Outstanding Faculty Award
  • 2011 | Society of Manufacturing Engineering Young Manufacturing Engineer Award
  • 2010 | NASA TechBrief Award
  • 2009 | National Science Foundation CAREER award


Discover more about Binil Starly



Systems and methods for authenticating manufacturing Machines through an unobservable fingerprinting system
Koprov, P., Gadhwala, S., Walimbe, A., Fang, X., & Starly, B. (2023), Manufacturing Letters, 35, 1009–1018.
“Unreal” factories: Next generation of digital twins of machines and factories in the Industrial Metaverse
Starly, B., Koprov, P., Bharadwaj, A., Batchelder, T., & Breitenbach, B. (2023), Manufacturing Letters, 37, 50–52.
Knowledge graph construction for product designs from large CAD model repositories
Bharadwaj, A. G., & Starly, B. (2022), ADVANCED ENGINEERING INFORMATICS, 53.
"FabNER": information extraction from manufacturing process science domain literature using named entity recognition
Kumar, A., & Starly, B. (2021, June 24), JOURNAL OF INTELLIGENT MANUFACTURING, Vol. 6.
A genetic algorithm for order acceptance and scheduling in additive manufacturing
Kapadia, M. S., Uzsoy, R., Starly, B., & Warsing, D. P., Jr. (2021, October 23), INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, Vol. 10.
Dynamic matching with deep reinforcement learning for a two-sided Manufacturing-as-a-Service (MaaS) marketplace
Pahwa, D., & Starly, B. (2021), MANUFACTURING LETTERS, 29, 11–14.
Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency
Hasan, M., Ogan, K., & Starly, B. (2021), 49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021), Vol. 53, pp. 594–605.
MVCNN plus plus : Computer-Aided Design Model Shape Classification and Retrieval Using Multi-View Convolutional Neural Networks
Angrish, A., Bharadwaj, A., & Starly, B. (2021), JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 21(1).
Non-destructive quality monitoring of 3D printed tissue scaffolds via dielectric impedance spectroscopy and supervised machine learning
Shohan, S., Harm, J., Hasan, M., Starly, B., & Shirwaiker, R. (2021), 49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021), Vol. 53, pp. 636–643.
Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis
Zhang, J., Zeng, Y., & Starly, B. (2021), SN APPLIED SCIENCES, 3(4).

View all publications via NC State Libraries

Binil Starly