Andrea Rovinelli
Andrea Rovinelli
Applied Materials Division, Argonne National Laboratory
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Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
A Rovinelli, MD Sangid, H Proudhon, W Ludwig
npj Computational Materials 4 (1), 1-10, 2018
Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations
A Rovinelli, MD Sangid, H Proudhon, Y Guilhem, RA Lebensohn, ...
Journal of the Mechanics and Physics of Solids 115, 208-229, 2018
Influence of microstructure variability on short crack behavior through postulated micromechanical short crack driving force metrics
A Rovinelli, RA Lebensohn, MD Sangid
Engineering Fracture Mechanics 138, 265-288, 2015
Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework
A Rovinelli, Y Guilhem, H Proudhon, RA Lebensohn, W Ludwig, ...
Modelling and Simulation in Materials Science and Engineering 25 (4), 045010, 2017
Assessing the reliability of fast Fourier transform-based crystal plasticity simulations of a polycrystalline material near a crack tip
A Rovinelli, H Proudhon, RA Lebensohn, MD Sangid
International Journal of Solids and Structures 184, 153-166, 2020
Validation of microstructure-based materials modeling
M Sangid, SR Yeratapally, A Rovinelli
55th AIAA/ASMe/ASCE/AHS/SC Structures, Structural Dynamics, and Materials …, 2014
Evaluation of statistical variation of microstructural properties and temperature effects on creep fracture of Grade 91
A Rovinelli, MC Messner, DM Parks, TL Sham
Argonne National Lab.(ANL), Argonne, IL (United States), 2018
Influence of microstructure variability on short crack growth behavior
A Rovinelli, MD Sangid, RA Lebensohn
Initial study of notch sensitivity of Grade 91 using mechanisms motivated crystal plasticity finite element method
A Rovinelli, MC Messner, G Ye, TL Sham
Argonne National Lab.(ANL), Argonne, IL (United States), 2019
Combining Experiments and Models via a Bayesian Network Approach to Predict Short Fatigue Crack Growth
A Rovinelli, MD Sangid, Y Guilhem, H Proudhon, R Lebensohn, ...
A General Probabilistic Framework Combining Experiments and Simulations to Identify the Small Crack Driving Force
A Rovinelli
Purdue University, 2017
Microstructurally-Short Crack Growth Driving Force Identification: Combining DCT, PCT, Crystal Plasticity Simulations and Machine Learning Technique
A Rovinelli, MD Sangid, RA Lebensohn, W Ludwig, Y Guilhem, ...
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