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Full-Text Articles in Engineering
Finite Element Modeling Of Meniscal Tears Using Continuum Damage Mechanics And Digital Image Correlation, Derek Q. Nesbitt, Dylan E. Burruel, Bradley S. Henderson, Trevor J. Lujan
Finite Element Modeling Of Meniscal Tears Using Continuum Damage Mechanics And Digital Image Correlation, Derek Q. Nesbitt, Dylan E. Burruel, Bradley S. Henderson, Trevor J. Lujan
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Meniscal tears are a common, painful, and debilitating knee injury with limited treatment options. Computational models that predict meniscal tears may help advance injury prevention and repair, but first these models must be validated using experimental data. Here we simulated meniscal tears with finite element analysis using continuum damage mechanics (CDM) in a transversely isotropic hyperelastic material. Finite element models were built to recreate the coupon geometry and loading conditions of forty uniaxial tensile experiments of human meniscus that were pulled to failure either parallel or perpendicular to the preferred fiber orientation. Two damage criteria were evaluated for all experiments: …
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 …