ASME

Journal of Computing and Information Science in Engineering

William 
Bernstein, Ph.D.
William Bernstein

Areas of Interest

CAD/CAM
DIGITAL MANUFACTURING
INDUSTRIAL AUGMENTED REALITY
INTEROPERABILITY
KNOWLEDGE REPRESENTATION
SUSTAINABLE DESIGN AND MANUFACTURING
Air Force Research Laboratory (AFRL)
Dr. William Z. Bernstein is the Branch Technical Advisor of the Digital Manufacturing and Supply Chain Branch in the Manufacturing and Industrial Technologies Division of the Materials and Manufacturing Directorate at the Air Force Research Laboratory (AFRL). He leads the Advanced Manufacturing Technologies (AMT) ManTech product portfolio, which comprises of the Additive Manufacturing, Digital Enterprise, and Future Factory programs. He is also a part of the RX Digital Manufacturing Research Team, which internally addresses challenges faced by the Air Force and Space Force services. His research interests are information modeling, system interoperability, and human-centered interfaces. He works on applications related to Computer-Aided Design and Manufacturing, Industrial Augmented Reality, Digital Manufacturing, and Sustainable Design and Manufacturing. He has published more than sixty technical articles in journals and refereed conference proceedings and has been awarded two US patents. He serves on the American Society of Mechanical Engineers (ASME) Design for Manufacturing and the Life Cycle (DFMLC) Technical Committee and on the Joint Defense Manufacturing Technology Panel (JDMTP) Advanced Manufacturing Enterprise (AME) Subpanel. He serves as an Associate Editor of the ASME Journal of Computing and Information Science in Engineering. He has received multiple honors and awards. Representative examples include the 2019 ASME Computers in Engineering (CIE) Young Engineer Award and a 2020 Department of Commerce (DOC) Bronze Medal Award for his contributions to ASTM Sustainable Manufacturing (E60) standards.

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June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

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