scholarly journals A Multifeatured Data-Driven Homogenization for Heterogeneous Elastic Solids

2021 ◽  
Vol 11 (19) ◽  
pp. 9208
Author(s):  
Ehsan Motevali Haghighi ◽  
Seonhong Na
Keyword(s):  
Finite Element ◽  
Material Properties ◽  
Data Driven ◽  
Porous Solids ◽  
Elastic Solids ◽  
Fe Simulations ◽  
Data Driven Approach ◽  
Micro Level ◽  
Elastic Responses ◽  
Nonlinear Elastic

A computational homogenization of heterogeneous solids is presented based on the data-driven approach for both linear and nonlinear elastic responses. Within the Double-Scale Finite Element Method (FE2) framework, a data-driven model is proposed to substitute the micro-level Finite Element (FE) simulations to reduce computational costs in multiscale simulations. The heterogeneity of porous solids at the micro-level is considered in various material properties and geometrical attributes. For material properties, elastic constants, which are Lame’s coefficients, are subjected to be heterogeneous in the linear elastic responses. For geometrical features, different numbers, sizes, and locations of voids are considered to reflect the heterogeneity of porous solids. A database for homogenized microstructural responses is constructed from a series of micro-level FE simulations, and machine learning is used to train and test our proposed model. In particular, four geometrical descriptors are designed, based on N-probability and lineal-path functions, to clearly reflect the geometrical heterogeneity of various microstructures. This study indicates that a simple deep neural networks model can capture diverse microstructural heterogeneous responses well when given proper input sources, including the geometrical descriptors, are considered to establish a computational data-driven homogenization scheme.

Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-Lithography

2016 ◽  
Author(s):  
Jun Wang ◽  
Sonjoy Das ◽  
Chi Zhou ◽  
Rahul Rai
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Stress Distribution ◽  
Material Properties ◽  
Simulation Models ◽  
Data Driven ◽  
Bottom Up ◽  
Grid Mapping ◽  
Data Driven Approach ◽  
Fast Prediction

Developing cohesive finite element simulation models of the pull-up process in bottom-up stereo-lithography (SLA) system can significantly increase the reliability and through-put of the bottom-up SLA process. Pull-up process modeling investigates relation between motion profile and crack initialization and propagation during the separation process. However, finite element (FE) simulation of the pull-up process is computationally very expensive and time-consuming. This paper outlines a method to quickly predict the separation stress distribution based on 2D shape grid mapping and neural network. Sixteen cohesive FE models with various cross-section shapes form our database. Specific 2D shape grid mapping was utilized to describe each shape by generating a sorted binary vector. A backpropagation (BP) neural network was then trained using binary vectors, material properties, and FE simulated pull-up separation stress distribution. Given material properties, the trained model can then be used to predict the pull-up separation stress distribution of a new shape. The results demonstrate that the proposed data driven method can drastically reduce computing costs. The comparison between the predicted values by the data driven approach and simulated FE models verify the validity of the proposed method.

MAP123: A data-driven approach to use 1D data for 3D nonlinear elastic materials modeling

2019 ◽  
Vol 357 ◽  
pp. 112587 ◽  
Author(s):  
Shan Tang ◽  
Gang Zhang ◽  
Hang Yang ◽  
Ying Li ◽  
Wing Kam Liu ◽  
...  
Keyword(s):  
Data Driven ◽  
Elastic Materials ◽  
Materials Modeling ◽  
Data Driven Approach ◽  
Nonlinear Elastic

Effect of Indenter Geometries on Material Properties: A Finite Element Simulation

2011 ◽  
Vol 70 ◽  
pp. 219-224 ◽  
Author(s):  
J.J. Kang ◽  
A.A. Becker ◽  
W. Sun
Keyword(s):  
Finite Element ◽  
Elastic Modulus ◽  
Finite Element Simulation ◽  
Material Properties ◽  
Plastic Strains ◽  
Fe Simulations ◽  
Element Simulation ◽  
Different Types ◽  
Indentation Tests

In this study, numerical indentation tests are carried out to examine the sensitivity of FE solutions with respect to different types of substrate models. Axisymmetric, 3D-quarter and 3D-half geometry substrates with a perfectly sharp indenter are modelled. Numerical evaluations of three different indenters, namely Berkovich, Vickers and conical indenters with perfectly sharp tips are investigated. From the FE simulations, the loading-unloading curves can be obtained. From the slope of the unloading curve, the hardness and elastic modulus can be calculated by using the Oliver-Pharr method. The results are compared to investigate the effects of using different indenter geometries. The equivalent plastic strains and the effects of different face angles of the indenters are analysed.

scholarly journals Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids

2020 ◽  
Vol 1 ◽  
Author(s):  
Xiaolong He ◽  
Qizhi He ◽  
Jiun-Shyan Chen ◽  
Usha Sinha ◽  
Shantanu Sinha
Keyword(s):  
Search Algorithm ◽  
Data Driven ◽  
Benchmark Problems ◽  
Local Convexity ◽  
Elastic Solids ◽  
Local Data ◽  
Online Data ◽  
Material Data ◽  
Computer Methods ◽  
Nonlinear Elastic

Abstract As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver, based on which data-driven solutions are obtained through minimization procedures. This work develops a new material solver built upon the local convexity-preserving reconstruction scheme by He and Chen (2020) A physics-constrained data-driven approach based on locally convex reconstruction for noisy database. Computer Methods in Applied Mechanics and Engineering 363, 112791 to model anisotropic nonlinear elastic solids. In this approach, a two-level local data search algorithm for material anisotropy is introduced into the material solver in online data-driven computing. A material anisotropic state characterizing the underlying material orientation is used for the manifold learning projection in the material solver. The performance of the proposed data-driven framework with noiseless and noisy material data is validated by solving two benchmark problems with synthetic material data. The data-driven solutions are compared with the constitutive model-based reference solutions to demonstrate the effectiveness of the proposed methods.

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