A Universal Array Approach for Finite Elements With Continuously Inhomogeneous Material Properties and Curved Boundaries

2012 ◽  
Vol 60 (10) ◽  
pp. 4745-4756 ◽  
Author(s):  
Davood Ansari Oghol Beig ◽  
Jue Wang ◽  
Zhen Peng ◽  
Jin-Fa Lee
Keyword(s):  
Finite Elements ◽  
Material Properties ◽  
Inhomogeneous Material ◽  
Curved Boundaries ◽  
Universal Array

Computer-Aided Visioplasticity Solution to Axisymmetric Extrusion Through Curved Boundaries

1972 ◽  
Vol 94 (4) ◽  
pp. 1225-1230 ◽  
Author(s):  
A. H. Shabaik
Keyword(s):  
Material Properties ◽  
Extrusion Ratio ◽  
Strain Rates ◽  
Exit Section ◽  
Flow Function ◽  
Curved Boundaries ◽  
Circular Arc ◽  
Conical Die ◽  
Stress Components ◽  
Experimental Values

A procedure for smoothing the experimental values of the flow function ψ in axisymmetric extrusion through curved boundaries was developed. The analysis was applied to a 45 deg conical die with a 6:1 extrusion ratio and a circular arc of 0.33-in. radius and 0.033-in. land at the exit section. An analytical expression of ψ in terms of r and z was obtained and used in the calculation of velocity and strain rate components in axisymmetric extrusion of a superplastic of the eutectic of lead–tin. The stress components were obtained from the known values of the strain rates by considering equilibrium and plasticity equations and material properties.

scholarly journals Considering inhomogeneous material properties for stiffness and failure prediction of thin-walled additively manufactured parts

2021 ◽  
Vol 34 ◽  
pp. 78-86
Author(s):  
Sigfrid-Laurin Sindinger ◽  
David Marschall ◽  
Christoph Kralovec ◽  
Martin Schagerl
Keyword(s):  
Material Properties ◽  
Failure Prediction ◽  
Inhomogeneous Material ◽  
Thin Walled

Estimation of the Non-Linear Material Properties for a Finite Elements Model of the Human Body Parts Involved in Sitting

2002 ◽  
Author(s):  
Niels C. C. M. Moes ◽  
Imre Horva´th
Keyword(s):  
Finite Elements ◽  
Human Body ◽  
Contact Area ◽  
Material Properties ◽  
Current Model ◽  
Consumer Products ◽  
Optimum Shape ◽  
Maximum Pressure ◽  
Finite Elements Analysis ◽  
Finite Elements Model

An optimization procedure was developed to search for the ergonomically optimum shape of consumer products. The entity to be optimized is a Finite Elements Model of the human body. The modification variable is the pressure values in the contact area. The Finite Elements Model and the optimization procedures were developed for a sitting support without backrest or arm rests. The model consists of a simplified assembly of the upper leg and the buttock area. Three components are included: skin, bony parts and in between a matrix of soft tissue. This paper presents the construction of the Finite Elements Model, in particular the assessment of the material properties according to the James-Green-Simpson elasticity model, and the first results of the Finite Elements Analysis. The first experiments are to validate the model for material properties. To that end the coefficients of the elasticity model are needed that cause good agreement of the maximum pressure values in the contact area with the predicted values obtained by earlier published regression. A hypothetical dependency of the maximum interface pressure on the stiffness is introduced. The results confirmed a part of the hypothesis. The current model needs further elaboration to test the hypothesis completely and to obtain a valid assessment of the material properties.

A Method for the Validation of Predictive Computations Using a Stochastic Approach

2002 ◽  
Author(s):  
Roger Ghanem ◽  
Manuel Pellissetti
Keyword(s):  
Finite Elements ◽  
Data Collection ◽  
Probability Measure ◽  
Material Properties ◽  
Adaptive Mesh Refinement ◽  
Mesh Refinement ◽  
The State ◽  
Calibration Error ◽  
Similar Data ◽  
Data Refinement

The task of model validation deals with quantifying the extent to which predictions from a particular model can be relied upon as representatives of the true behavior of the system being modeled. This issue is of great importance in assessing the reliability and safety of structures since in most cases their quantification relies on predictions from sophisticated probabilistic models. The paper describes a formalism that will extend the realm of the model to include all aspects of data collection and parameter calibration. Error estimators are developed that permit the quantification of the value of computational efforts (mesh refinement) versus analytical efforts (model refinement) and experimental effort (data acquisition and analysis). Starting with the hypothesis that the material properties of a given medium can be modeled within the framework of probability theory, a very rich mathematical setting is available to completely characterize the probabilistic behavior and evolution of the associated random medium under an external disturbance. The probability measure on the material properties is uniquely transformed into a probability measure on the state of the medium. A computational implementation of related concepts has been developed in the framework of stochastic finite elements and applied to a number of problems. Clearly, great value can be attributed to the ability of performing the forward analysis whereby the probability measure on the state of the system is completely characterized by the measure on the material properties. The assumed probability measure on the material properties, however, is greatly dependent on the amount and quality of data used to synthesize this measure. As this measure is updated, estimates of the performance of the underlying natural or physical system change. Significant interest exists therefore in developing the capability of controlling the error in the probabilistic estimates through designed data collection. The mathematical setting adopted in this paper for describing random variables is ideally suited for treating this problem as one of data refinement. A close parallel will be delineated between this concept and that of adaptive mesh refinement, well established in deterministic finite elements. Underlying this latter problem, are issues related to error estimation that are relied upon to guide the adaptation of the refinement. The paper develops a similar “data refinement” concept and highlights the basic underlying principles.

Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

2020 ◽  
Author(s):  
Xuehai Wu ◽  
Assimina A. Pelegri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
White Matter ◽  
Finite Elements ◽  
Frequency Domain ◽  
Finite Element Methods ◽  
Material Properties ◽  
Output Data ◽  
Micro Scale ◽  
Brain White Matter

Abstract Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature.

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