Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations

[1]  Qi Zhang,et al.  Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters , 2023, Computer Methods in Applied Mechanics and Engineering.

[2]  A. Beese,et al.  Density functional theory-informed dislocation density hardening within crystal plasticity: Application to modeling deformation of Ni polycrystals , 2022, Computational Materials Science.

[3]  S. Choi,et al.  Reconstructing orientation data from the images of IPF maps and ODF sections extracted from the literature: A data-collection method for machine learning , 2022, International Journal of Plasticity.

[4]  J. Allison,et al.  PRISMS-Plasticity TM: An Open-Source Rapid Texture Evolution Analysis Pipeline , 2022, Integrating Materials and Manufacturing Innovation.

[5]  Huaiju Liu,et al.  A micromechanics-based machine learning model for evaluating the microstructure-dependent rolling contact fatigue performance of a martensitic steel , 2022, International Journal of Mechanical Sciences.

[6]  D. Mohr,et al.  From CP-FFT to CP-RNN: Recurrent Neural Network Surrogate Model of Crystal Plasticity , 2022, International Journal of Plasticity.

[7]  S. Zapperi,et al.  Predicting creep failure by machine learning - which features matter? , 2022, Forces in Mechanics.

[8]  Somnath Ghosh,et al.  Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys , 2022, Computer Methods in Applied Mechanics and Engineering.

[9]  W. Muhammad,et al.  A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals , 2022, International Journal of Plasticity.

[10]  A. Tran,et al.  Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods , 2022, Frontiers in Materials.

[11]  Krzysztof S. Stopka,et al.  Crystal plasticity finite element modeling of grain size and morphology effects on yield strength and extreme value fatigue response , 2022, Journal of Materials Research and Technology.

[12]  M. Knezevic,et al.  Coupling of a multi-GPU accelerated elasto-visco-plastic fast Fourier transform constitutive model with the implicit finite element method , 2022, Computational Materials Science.

[13]  L. Mushongera,et al.  Prediction of Cyclic Damage in Metallic Alloys with Crystal Plasticity Modeling enhanced by Machine Learning , 2022, Materialia.

[14]  Péter Bayer,et al.  The Shapley Value in Machine Learning , 2022, IJCAI.

[15]  Minsheng Huang,et al.  A deep learning method for predicting microvoid growth in heterogeneous polycrystals , 2022, Engineering Fracture Mechanics.

[16]  K. Hazeli,et al.  Understanding slip activity and void initiation in metals using machine learning-based microscopy analysis , 2022, Materials Science and Engineering: A.

[17]  Jan N. Fuhg,et al.  Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations , 2022, Materialia.

[18]  M. Diehl,et al.  Predicting grain boundary damage by machine learning , 2021, International Journal of Plasticity.

[19]  Andrew C. Parnell,et al.  Towards an Instant Structure-Property Prediction Quality Control Tool for Additive Manufactured Steel using a Crystal Plasticity Trained Deep Learning Surrogate , 2021, Materials & Design.

[20]  J. Segurado,et al.  FFT based approaches in micromechanics: fundamentals, methods and applications , 2021, Modelling and Simulation in Materials Science and Engineering.

[21]  S. Niezgoda,et al.  Comparison of full field predictions of crystal plasticity simulations using the Voce and the dislocation density based hardening laws , 2021, International Journal of Plasticity.

[22]  W. Muhammad,et al.  A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths , 2021, International Journal of Plasticity.

[23]  Isaac Tamblyn,et al.  Deep learning and crystal plasticity: A preconditioning approach for accurate orientation evolution prediction , 2021, Computer Methods in Applied Mechanics and Engineering.

[24]  G. Pilania Machine learning in materials science: From explainable predictions to autonomous design , 2021, Computational Materials Science.

[25]  Manoj Khandelwal,et al.  Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration , 2021, Engineering with Computers.

[26]  Xin Sun,et al.  A finite element formulation for deformation twinning induced strain localization in polycrystal magnesium alloys , 2021 .

[27]  A. Beese,et al.  Identification of stress state dependent fracture micromechanisms in DP600 through representative volume element modeling , 2021 .

[28]  Sophie Lambert-Lacroix,et al.  Random forests for global sensitivity analysis: A selective review , 2021, Reliab. Eng. Syst. Saf..

[29]  R. Lebensohn,et al.  Modeling the role of local crystallographic correlations in microstructures of Ti-6Al-4V using a correlated structure visco-plastic self-consistent polycrystal plasticity formulation , 2021 .

[30]  Scott M. Lundberg,et al.  Understanding Global Feature Contributions With Additive Importance Measures , 2020, NeurIPS.

[31]  Wasim Ahmad,et al.  A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series , 2020 .

[32]  M. Knezevic,et al.  A full-field crystal plasticity model including the effects of precipitates: Application to monotonic, load reversal, and low-cycle fatigue behavior of Inconel 718 , 2020 .

[33]  Adnan Eghtesad,et al.  A multi-GPU implementation of a full-field crystal plasticity solver for efficient modeling of high-resolution microstructures , 2020, Comput. Phys. Commun..

[34]  Abdul Rehman Javed,et al.  Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles , 2020, Trans. Emerg. Telecommun. Technol..

[35]  Tzu-Tsung Wong,et al.  Reliable Accuracy Estimates from k-Fold Cross Validation , 2020, IEEE Transactions on Knowledge and Data Engineering.

[36]  Ashley D. Spear,et al.  Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods , 2020, Computational Materials Science.

[37]  A. Ghaderi,et al.  Five-parameter grain boundary characterisation of randomly textured AZ31 Mg alloy , 2020 .

[38]  A. Rollett,et al.  Spectral methods for full-field micromechanical modelling of polycrystalline materials , 2020 .

[39]  T. Bieler,et al.  A criterion for slip transfer at grain boundaries in Al , 2019, 1912.02925.

[40]  S. Donegan,et al.  Associating local microstructure with predicted thermally-induced stress hotspots using convolutional neural networks , 2019 .

[41]  Gonzalo Martínez-Muñoz,et al.  A comparative analysis of gradient boosting algorithms , 2019, Artificial Intelligence Review.

[42]  Veera Sundararaghavan,et al.  PRISMS-Plasticity: An open-source crystal plasticity finite element software , 2019, Computational Materials Science.

[43]  Somnath Ghosh,et al.  Parametrically homogenized constitutive models (PHCMs) from micromechanical crystal plasticity FE simulations: Part II: Thermo-elasto-plastic model with experimental validation for titanium alloys , 2019, International Journal of Plasticity.

[44]  A. Smilde,et al.  Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models , 2019, Scientific Reports.

[45]  Tin Kam Ho,et al.  Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language , 2019, Journal of Educational and Behavioral Statistics.

[46]  Adnan Eghtesad,et al.  OpenMP and MPI implementations of an elasto-viscoplastic fast Fourier transform-based micromechanical solver for fast crystal plasticity modeling , 2018, Adv. Eng. Softw..

[47]  M. Diehl,et al.  Spectral Solvers for Crystal Plasticity and Multi-physics Simulations , 2018, Handbook of Mechanics of Materials.

[48]  J. Ranstam,et al.  LASSO regression , 2018, The British journal of surgery.

[49]  Bohdan Pavlyshenko,et al.  Using Stacking Approaches for Machine Learning Models , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[50]  Aleksander Karolczuk,et al.  Evaluation of the Fatemi-Socie damage parameter for the fatigue life calculation with application of the Chaboche plasticity model , 2018, Fatigue & Fracture of Engineering Materials & Structures.

[51]  S. Nikolov,et al.  DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale , 2018, Computational Materials Science.

[52]  Elizabeth A. Holm,et al.  A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials , 2018, Integrating Materials and Manufacturing Innovation.

[53]  E. Holm,et al.  Applied machine learning to predict stress hotspots II: Hexagonal close packed materials , 2018, International Journal of Plasticity.

[54]  E. Holm,et al.  Applied machine learning to predict stress hotspots I: Face centered cubic materials , 2017, International Journal of Plasticity.

[55]  F. Dunne,et al.  An HR-EBSD and computational crystal plasticity investigation of microstructural stress distributions and fatigue hotspots in polycrystalline copper , 2016 .

[56]  Carlos Guestrin,et al.  Model-Agnostic Interpretability of Machine Learning , 2016, ArXiv.

[57]  I. Beyerlein,et al.  A study of microstructure-driven strain localizations in two-phase polycrystalline HCP/BCC composites using a multi-scale model , 2015 .

[58]  A. McBride,et al.  Review on slip transmission criteria in experiments and crystal plasticity models , 2015, Journal of Materials Science.

[59]  Alok Choudhary,et al.  A predictive machine learning approach for microstructure optimization and materials design , 2015, Scientific Reports.

[60]  S. Kalidindi,et al.  Crystal plasticity finite element simulations using a database of discrete Fourier transforms , 2015 .

[61]  I. Beyerlein,et al.  Three dimensional predictions of grain scale plasticity and grain boundaries using crystal plasticity finite element models , 2014 .

[62]  Sotiris B. Kotsiantis,et al.  Decision trees: a recent overview , 2011, Artificial Intelligence Review.

[63]  Philip Eisenlohr,et al.  An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials , 2012 .

[64]  A. Rollett,et al.  Stress hot spots in viscoplastic deformation of polycrystals , 2010 .

[65]  Carlos N. Tomé,et al.  A dislocation-based constitutive law for pure Zr including temperature effects , 2008 .

[66]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[67]  Nicholas Zabaras,et al.  Classification and reconstruction of three-dimensional microstructures using support vector machines , 2005 .

[68]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[69]  R. Mccabe,et al.  An automated procedure built on MTEX for reconstructing deformation twin hierarchies from electron backscattered diffraction datasets of heavily twinned microstructures , 2021 .

[70]  M. Knezevic,et al.  High-performance full-field crystal plasticity with dislocation-based hardening and slip system back-stress laws: Application to modeling deformation of dual-phase steels , 2020 .

[71]  M. Daymond,et al.  Orientation-dependent irradiation hardening in pure Zr studied by nanoindentation, electron microscopies, and crystal plasticity finite element modeling , 2020 .