On the potential of recurrent neural networks for modeling path dependent plasticity
暂无分享,去创建一个
[1] D. Mohr,et al. Neural network model describing the temperature- and rate-dependent stress-strain response of polypropylene , 2020 .
[2] D. Mohr,et al. Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling , 2020 .
[3] Maysam Gorji,et al. Towards neural network models for describing the large deformation behavior of sheet metal , 2019, IOP Conference Series: Materials Science and Engineering.
[4] Lars Greve,et al. Necking-induced fracture prediction using an artificial neural network trained on virtual test data , 2019, Engineering Fracture Mechanics.
[5] Usman Ali,et al. Application of artificial neural networks in micromechanics for polycrystalline metals , 2019, International Journal of Plasticity.
[6] Christian C. Roth,et al. Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel , 2019, International Journal of Plasticity.
[7] R. Jones,et al. Predicting the mechanical response of oligocrystals with deep learning , 2019, Computational Materials Science.
[8] Zeliang Liu,et al. Exploring the 3D architectures of deep material network in data-driven multiscale mechanics , 2019, Journal of the Mechanics and Physics of Solids.
[9] T. Tancogne-Dejean,et al. Hosford-Coulomb ductile failure model for shell elements: Experimental identification and validation for DP980 steel and aluminum 6016-T4 , 2018, International Journal of Solids and Structures.
[10] Zeliang Liu,et al. A Deep Material Network for Multiscale Topology Learning and Accelerated Nonlinear Modeling of Heterogeneous Materials , 2018, Computer Methods in Applied Mechanics and Engineering.
[11] Wolfgang Ludwig,et al. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials , 2018, npj Computational Materials.
[12] W. Ludwig,et al. Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations , 2018, Journal of the Mechanics and Physics of Solids.
[13] T. Tancogne-Dejean,et al. Heterogeneous random medium plasticity and fracture model of additively-manufactured Ti-6A1-4V , 2018 .
[14] T. Nguyen,et al. Interaction of rate- and size-effect using a dislocation density based strain gradient viscoplasticity model , 2017 .
[15] J. Imbert,et al. Effects of coupling anisotropic yield functions with the optimization process of extruded aluminum front rail geometries in crashworthiness , 2017 .
[16] D. Mohr,et al. Paint-bake effect on the plasticity and fracture of pre-strained aluminum 6451 sheets , 2017 .
[17] Michael J. Worswick,et al. Development of high crush efficient, extrudable aluminium front rails for vehicle lightweighting , 2016 .
[18] Daniel E. Green,et al. The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets , 2016 .
[19] D. Dini,et al. The mechanisms governing the activation of dislocation sources in aluminum at different strain rates , 2015 .
[20] Frédéric Barlat,et al. Enhancements of homogenous anisotropic hardening model and application to mild and dual-phase steels , 2014 .
[21] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[22] Frédéric Barlat,et al. EXTENSION OF HOMOGENEOUS ANISOTROPIC HARDENING MODEL TO CROSS-LOADING WITH LATENT EFFECTS , 2013 .
[23] Frédéric Barlat,et al. An alternative to kinematic hardening in classical plasticity , 2011 .
[24] Tomonari Furukawa,et al. Neural network constitutive modelling for non‐linear characterization of anisotropic materials , 2011 .
[25] T. Belytschko,et al. Thermal softening induced plastic instability in rate-dependent materials , 2009 .
[26] Hoan-Kee Kim,et al. Nonlinear constitutive models for FRP composites using artificial neural networks , 2007 .
[27] Frédéric Barlat,et al. Work-hardening model for polycrystalline metals under strain reversal at large strains , 2007 .
[28] Hamid Garmestani,et al. Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network , 2006 .
[29] F. Barlat,et al. Plane stress yield function for aluminum alloy sheets—part 1: theory , 2003 .
[30] Frédéric Barlat,et al. Plastic flow for non-monotonic loading conditions of an aluminum alloy sheet sample , 2003 .
[31] M. Lefik,et al. Artificial neural network as an incremental non-linear constitutive model for a finite element code , 2003 .
[32] Fusahito Yoshida,et al. A model of large-strain cyclic plasticity describing the Bauschinger effect and workhardening stagnation , 2002 .
[33] F. Mollica. The inelastic behavior of metals subject to loading reversal , 2001 .
[34] Magdalena Ortiz,et al. A micromechanical model of hardening, rate sensitivity and thermal softening in BCC single crystals , 2001, cond-mat/0103284.
[35] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[36] A. P. Karafillis,et al. A general anisotropic yield criterion using bounds and a transformation weighting tensor , 1993 .
[37] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[38] O. Sherby,et al. Large strain deformation of polycrystalline metals at low homologous temperatures , 1975 .
[39] R. Hill. A theory of the yielding and plastic flow of anisotropic metals , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.
[40] Dirk Mohr,et al. Predicting shear fracture of aluminum 6016-T4 during deep drawing: Combining Yld-2000 plasticity with Hosford Coulomb fracture model , 2018 .
[41] Geoffrey E. Hinton,et al. Deep Learning , 2015 .
[42] Philip Raoul Peters,et al. Yield Functions taking into account Anisotropic Hardening Effects for an Improved Virtual Representation of Deep Drawing Processes , 2015 .
[43] A. Rauh,et al. A NEURAL NETWORK BASED ELASTO-PLASTICITY MATERIAL MODEL , 2012 .
[44] Lallit Anand,et al. Elasto-viscoplastic constitutive equations for polycrystalline fcc materials at low homologous temperatures , 2002 .
[45] S. Solla,et al. Consistent and Minimal Springback Using a Stepped Binder Force Trajectory and Neural Network Control , 2000 .
[46] R. Hill. Constitutive modelling of orthotropic plasticity in sheet metals , 1990 .
[47] J. Chaboche. Constitutive equations for cyclic plasticity and cyclic viscoplasticity , 1989 .
[48] William F. Hosford,et al. Upper-bound anisotropic yield locus calculations assuming 〈111〉-pencil glide , 1980 .
[49] K. J. Marsh,et al. The effect of strain rate on the post-yield flow of mild steel , 1963 .
[50] W. Prager,et al. A NEW METHOD OF ANALYZING STRESSES AND STRAINS IN WORK - HARDENING PLASTIC SOLIDS , 1956 .