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Artificially Intelligent Manufacturing (AIM)

Advanced materials and manufacturing are important pillars and drivers of today’s economy. They have made consumer devices like TVs and computers ubiquitous by enabling cheap mass production of reliable products, and enabled industrial applications on a scale not seen before. However, designing new materials is a slow and laborious process, requiring significant investments in capital and labor.

Recent advances in artificial intelligence allow to improve and speed up this process. In particular, machine learning techniques can replace expensive and time-consuming laboratory processes and computational simulations to quickly and reliably predict how to create materials with desired properties and how materials will behave in specific circumstances. So-called surrogate models learn how properties of materials and experimental parameters relate to experimental results.

AIM applies these techniques to materials engineering and manufacturing to reduce cost and improve performance. The center merges synergistic expertise at the College of Engineering and Applied Sciences in materials development, chemical engineering, mechanical engineering, and computer science for the development of powerful methods to design and model the behavior of advanced materials and manufacture advanced devices.

Projects

Graphene Oxide Film Graphitization via Laser Patterning

Graphene oxide can be treated with lasers to create patterns of conducting carbon within a surrounding environment of insulating materials. This allows to create nano-scale circuits in thin sheets of materials, enabling next-generation devices. Laser patterning is a complex process that requires precise real-time control to achieve the desired results. We apply techniques from artificial intelligence to achieve the necessary precise control.

Composite Failure Prediction

Determining the limits of forces that can be applied to composite materials before they break is an important problem in engineering and manufacturing. The field has developed a number of failure criteria, which allow to describe those limits in a closed functional form, so called failure envelopes. However, these functional forms are not very flexible, and require significant experimentation with new materials to be determined. We apply machine learning techniques for more flexible failure envelopes and to predict them based on very little data for new materials.

 

PUBLICATIONS

  • Bhuiyan, Faisal H., Lars Kotthoff, and Ray S. Fertig. "A Machine Learning Technique to Predict Static Multi-Axial Failure Envelope of Laminated Composites." In American Society for Composites 33rd Annual Technical Conference, 2018. preprint (PDF)

PEOPLE

PIs


POSTDOCTORAL RESEARCHERS

  • Maedeh Beheshti
  • Hud Wahab

STUDENTS

  • Faisal Bhuiyan
  • Vivek Jain
  • Gaurav Raj
  • Alex Tyrrell

CONTACT

Lars Kotthoff, larsko@uwyo.edu

1000 E. University Ave. Laramie, WY 82071
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