People

Jack Beuth received his Ph.D. in Engineering Sciences from Harvard in 1992. He has been a member of the Carnegie Mellon faculty since that time. Beuth’s research is in the areas of manufacturing, solid mechanics, and fracture mechanics, with over 75 publications across the areas of additive manufacturing, interfacial mechanics, and thin film mechanics. His current research includes modeling of additive manufacturing processes and micro-scale

Beuth was a recipient of the 1998 Ralph R. Teetor Educational Award. In 2000, he was awarded George Tallman and Florence Barrett Ladd Development Professorship in Mechanical Engineering. In 2005, Beuth was co-recipient of the ASME Curriculum Innovation Award. In 2009, Beuth received the Benjamin Richard Teare Teaching Award from the College of Engineering.

Beuth’s modeling research in additive manufacturing has led to the development of “process map” approaches for mapping out the role of principal process variables on process characteristics such as melt pool geometry, microstructure, and residual stress. By characterizing AM processes over their full process variable range, Beuth’s research is allowing unique insights into process control, expansion of process operating ranges, and unique comparisons of AM processes operating in very different regions of processing space.

Office
302 Scaife Hall
Phone
412.268.3873
Email
beuth@andrew.cmu.edu
Websites
Beuth’s Additive Lab Opens in new window

Process Mapping for Additive Manufacturing

Education

1992 Ph.D., Engineering Sciences, Harvard University

1989 MS, Engineering Sciences, Harvard University

1987 MS, Engineering Science and Mechanics, Virginia Institute of Technology

1984 BS, Engineering Science and Mechanics, Virginia Institute of Technology

Media mentions


Mechanical Engineering

Deep learning alternative to monitoring LPBF

Novel deep learning pipeline provides a low-cost, scalable alternative for manufacturers looking to monitor LPBF melt pools.

CMU Engineering

AI accelerates process design for 3D printing metal alloys

Researchers use AI and high-speed in-situ imaging to optimize process parameters for 3D printing metal alloys.

CMU Engineering

Scaling in-situ process monitoring to qualify AM parts

Mechanical Engineering alumnus Luke Scime uses artificial intelligence in the development of Peregrine, a process monitoring software stack to qualify additive manufactured parts at the U.S. national laboratories.

CMU Engineering

Accelerating adoption of additive manufacturing

Engineering alumnus Brian Fisher embodies the vision for additive manufacturing his former professor Jack Beuth helped him to see.