Glenn Daehn
Ohio State University, OH·Engineering
Making a metal part today usually means choosing between speed and flexibility. Mass-production methods like closed-die forging, where metal is hammered into a shaped die, give strong parts but require expensive tooling and long lead times, while flexible methods struggle to guarantee that every part meets specification.
- Fields
Engineering
Materials Science
Computer Science
- Topics
hybrid manufacturing
autonomous manufacturing
advanced manufacturing
- Grants
- 1 known grant, through Aug 2029
What they work on
- Develop numerically controlled deformation sequences and equipment to produce complex components currently made as closed-die forgings, where metal is pressed into shaped tooling, with the goal of shorter lead times and improved part performance.
- Use numerically controlled deformation to locally optimize material properties within additively manufactured (3D-printed) components, tailoring microstructure position by position rather than uniformly.
- Build control, autonomy, and machine intelligence approaches that guide hybrid multi-tool manufacturing systems using robotics and sensor data, so systems learn from prior runs and improve in efficiency with experience.
- Assure part quality by predicting local material structure and properties during processing within quantified uncertainty limits, integrating computational materials engineering with design and tooling in one framework.
- Extend automated deformation operations toward point-of-care manufacturing that rapidly tailors medical devices to an individual patient's anatomy.
- Create low-cost desktop training systems that give students hands-on experience programming, operating, and maintaining hybrid manufacturing equipment and making products by incremental deformation.
More about the research
The question driving this center is whether manufacturing machines can be made agile and self-improving: able to combine several tools and processes in one workflow, adjust as they go, and certify the quality of what they produce. Work here develops hybrid, autonomous manufacturing systems that shape components while deliberately controlling the material's internal structure and properties, location by location.
Why it matters
Manufacturers currently wait months and pay for dedicated tooling to produce complex metal parts, which limits customization and domestic production of small batches. Hybrid autonomous systems that assure quality by predicting material properties could shorten lead times, enable medical devices shaped to an individual patient's anatomy at the point of care, and support cost-effective local production. The center also trains technicians and engineers and develops certification standards for these technologies.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yanfei Gao
University of Tennessee-Knoxville, TN·Materials Science
Examine how materials and manufacturing processes affect the mechanical, functional and environmental performance of welded or 3D-printed metal parts, to improve reliability and extend the service life of critical infrastructure.
Jun Wang
Santa Clara University, CA·Engineering
Extend the known theoretical limits of the mechanical property space reachable by aperiodic architected cellular material (AACM) units using a computational discovery framework.
Yiwei Han
University of Mississippi, MS·Engineering·Early-career
Determine how extrusion-printed shape-changing structures affect electrohydrodynamic (EHD) printing, a high-resolution method that jets fine droplets with an electric field, and how EHD deposition speed affects resolution and impact damage.
The school
Ohio State University: 71 labs on PI Pages, $1.6B a year on research, 882 research doctorates a year.
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