• MMM Lab Wins NIST AM Bench Challenge

    The team won first place in predicting subcontinuum mechanical response of as-built IN625 manufactured by laser powder bed fusion.

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  • Effect of Grain Structure on Crush Response of Open-Cell Metal Foam

    High-fidelity numerical simulations offer new insight into the dependence of crush strength on grain size and surface oxidation for open-cell metal foams. Results are used to extend the Gibson-Ashby formula.

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    ML+MD to predict strength of CNT-polymer interfaces

    In collaboration with researchers from Michigan Tech, the MMM Lab recently published a paper describing the integration of molecular dynamics and machine learning to predict mechanical properties of carbon nanotube composites.

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  • Deep learning to predict microstructure-property relationships in AM metals

    Our recent work using machine and deep learning to predict microstructure-dependent property maps for AM metals is now published in Computational Materials Science.

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  • Predicting skull fracture in infants

    Our work on predicting impact-induced skull fracture in infants is now available in the journal Biomechanics and Modeling in Mechanobiology.

    The work is funded by the Department of Justice.

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  • Machine Learning Model Predicts 3D Crack Path

    MMM Lab members implemented a convolutional neural network to predict growth of a 3D crack surface and to quantify the corresponding model uncertainty.

    Click below to find out more!

    Link to paper

  • Fracture Mechanics

    The group specializes in characterizing and simulating 3-D fracture and fatigue, which play an important role in structural prognosis and materials design and optimization.

  • Multiscale Modeling & Materials Characterization

    Multiscale modeling techniques are employed to capture the mechanics acting across multiple length scales. Currently, the group focuses on concurrent multiscale modeling from microstructure to component length scales.

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Multiscale Mechanics & Materials Lab

Our group conducts cutting-edge research at the nexus of materials and structures. We couple materials characterization with high-performance computing and data-driven analysis (including machine learning) to address a wide range of research topics that are especially pertinent to the defense, aerospace, and manufacturing communities.

Philosophy

We believe that by pursuing a 3-D understanding of material and mechanical behavior across multiple length scales, we can begin to design smarter, multifunctional, and more sustainable structural systems to suit the needs of a dynamic society.

Tools & Techniques

The MMM Lab employs various types of computing resources to simulate and predict 3-D mechanical behavior of materials, with emphasis on fatigue and fracture. We support numerical modeling with advanced materials characterization techniques, including scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and high-energy X-ray diffraction microscopy (HEDM) to characterize and quantify material structure at lower length scales. Finally, we develop methods to analyze, visualize, and effectively communicate the 3-D information obtained from our measurements and simulations.

Recent Activity

MMM Lab welcomes five new members

MMM Lab welcomes five new members

The MMM Lab recently welcomed Dr. Krishna Prasath Logakannan, Claire Ticknor, Jake Hirst, Bjorn Johnsson, and Allie Richards.   Dr. Krishna Logakannan joins the lab as a postdoctoral researcher [...]

Spear Named University Presidential Scholar

Spear Named University Presidential Scholar

The annual award recognizes excellence and achievement for faculty members at the assistant or associate professor level and comes with $10,000 in annual funding for three years [...]

Congratulations to Dr. Karen DeMille and Carter Cocke!

Congratulations to Dr. Karen DeMille and Carter Cocke!

We are pleased to announce the graduation of MMM Lab members Karen DeMille and Carter Cocke. Karen DeMille successfully defended her Ph.D. dissertation entitled Establishment of Representative Volume [...]