Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There …
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition …
Nuclear Envelope Mechanobiology: Linking The Nuclear Structure And Function, Matthew Goelzer, Julianna Goelzer, Matthew L. Ferguson, Corey P. Neu, Gunes Uzer
Nuclear Envelope Mechanobiology: Linking The Nuclear Structure And Function, Matthew Goelzer, Julianna Goelzer, Matthew L. Ferguson, Corey P. Neu, Gunes Uzer
Mechanical and Biomedical Engineering Faculty Publications and Presentations
The nucleus, central to cellular activity, relies on both direct mechanical input as well as its molecular transducers to sense external stimuli and respond by regulating intra-nuclear chromatin organization that determines cell function and fate. In mesenchymal stem cells of musculoskeletal tissues, changes in nuclear structures are emerging as a key modulator of their differentiation and proliferation programs. In this review we will first introduce the structural elements of the nucleoskeleton and discuss the current literature on how nuclear structure and signaling are altered in relation to environmental and tissue level mechanical cues. We will focus on state-of-the-art techniques to …
Integration Of Neural Architecture Within A Finite Element Framework For Improved Neuromusculoskeletal Modeling, Victoria L. Volk, Landon D. Hamilton, Donald R. Hume, Kevin B. Shelburne, Clare K. Fitzpatrick
Integration Of Neural Architecture Within A Finite Element Framework For Improved Neuromusculoskeletal Modeling, Victoria L. Volk, Landon D. Hamilton, Donald R. Hume, Kevin B. Shelburne, Clare K. Fitzpatrick
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding …
Three-Dimensional Phase Field Modeling Of Fracture In Shape Memory Ceramics, Ehsan Moshkelgosha, Mahmood Mamivand
Three-Dimensional Phase Field Modeling Of Fracture In Shape Memory Ceramics, Ehsan Moshkelgosha, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Despite the vast applications of transformable ceramics, such as zirconia-based ceramics, in different areas from biomedical to aerospace, the fundamental knowledge about their mechanical degradation procedure is limited. The interaction of the phase transformation and crack growth is crucial as the essential underlying mechanism in fracture of these transformable ceramics, also known as shape memory ceramics. This study develops a three-dimensional (3D) multiphysics model that couples the variational formulation of brittle crack growth to the Ginzburg-Landau equations of martensitic transformation. We parameterized the model for the 3D single crystal zirconia, which experienced stress- and thermal-induced tetragonal to monoclinic transformation. The …
The Party’S Over: Sustaining Support Programs When The Funding Is Done, John Gardner, Pat Pyke, Cheryl Schrader, Janet M. Callahan, Amy Moll
The Party’S Over: Sustaining Support Programs When The Funding Is Done, John Gardner, Pat Pyke, Cheryl Schrader, Janet M. Callahan, Amy Moll
Mechanical and Biomedical Engineering Faculty Publications and Presentations
In the lifecycle of an engineering education grant, the phase where best practices are sustained and disseminated is perhaps the most crucial stage for maximizing impact. Yet this transition phase often receives the least attention as project team enthusiasm can wane, while funding tapers off, and faculty priorities are pulled in other directions. There are numerous obstacles associated with sustaining program changes, even those perceived as very valuable. Typical challenges are: What happens when the funding runs out? What grant-developed programs should be sustained by the university? Does the institution need to internally allocate resources in an annual budget large …