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.

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.

Study of Pullout Performance of Self-Tapping Screws for Human Bone

2011 ◽  
Author(s):  
Zhijun Wu ◽  
Sayed A. Nassar ◽  
Xianjie Yang
Keyword(s):  
Finite Element ◽  
Stress Distribution ◽  
Analytical Model ◽  
Material Properties ◽  
Pedicle Screws ◽  
Pullout Strength ◽  
Bone Material ◽  
Synthetic Bone ◽  
Failure Propagation ◽  
Device Applications

The study investigates the pullout strength of self-tapping pedicle screws using analytical, finite element, and experimental methodologies with focus on medical device applications. The stress distribution and failure propagation around implant threads in the synthetic bone during the pullout process, as well as the pullout strength of pedicle screws, are explored. Based on the FEA results, an analytical model for the pullout strength of the pedicle screw is constructed in terms of the synthetic bone material properties, screw size, and implant depth. The characteristics of pullout behavior of self-tapping pedicle screws are discussed. Both the analytical model and finite element results are validated using experimental techniques.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

scholarly journals Application of deep neural network reveals novel effects of maternal pre-conception exposure to nicotine on rat pup behavior

2020 ◽  
Author(s):  
Reza Torabi ◽  
Serena Jenkins ◽  
Allonna Harker ◽  
Ian Q. Whishaw ◽  
Robbin Gibb ◽  
...  
Keyword(s):  
Neural Network ◽  
Motor Development ◽  
Deep Neural Network ◽  
Data Driven ◽  
Nicotine Exposure ◽  
Movement Initiation ◽  
Warm Up ◽  
Rat Pups ◽  
The Neural Network ◽  
Data Driven Approach

We present a deep neural network for data-driven analyses of infant rat behavior in an open field task. The network was applied to study the effect of maternal nicotine exposure prior to conception on offspring motor development. The neural network outperformed human expert designed animal locomotion measures in distinguishing rat pups born to nicotine exposed dams versus control dams. Notably, the network discovered novel movement alterations in posture, movement initiation and a stereotypy in warm-up behavior (the initiation of movement along specific dimensions) that were predictive of nicotine exposure. The results suggest that maternal preconception nicotine exposure delays and alters offspring motor development. In summary, we demonstrated that a deep neural network can automatically assess animal behavior with high accuracy, and that it offers a data-driven approach to investigating pharmacological effects on brain development.

scholarly journals Material property and boundary condition effects on stresses in avalanche snow-packs

1974 ◽  
Vol 13 (67) ◽  
pp. 99-108 ◽  
Author(s):  
J. O. Curtis ◽  
F. W. Smith
Keyword(s):  
Boundary Condition ◽  
Finite Element ◽  
Stress Distribution ◽  
Computer Program ◽  
Material Properties ◽  
Shear Failure ◽  
Stress Distributions ◽  
Snow Pack ◽  
Snow Layer ◽  
Linear Elastic

A linear elastic finite element computer program was applied to determine the stress distributions in multi-layered snow-packs typical of those found at Berthoud Pass, Colorado. The effect on stress distribution of wide variations in elastic material properties was examined. Also, an attempt was made to model the shear failure of a weak sub-layer in the snow-pack by relaxing the condition that the bottom snow layer be firmly attached to the ground.

scholarly journals ‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Nidhal Selmi ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Network ◽  
Chemical Space ◽  
Ring System ◽  
Data Driven ◽  
Ring Systems ◽  
The Neural Network ◽  
Synthetic Accessibility ◽  
Synthesis Planning ◽  
Data Driven Approach ◽  
Synthetic Routes

<p></p><p>Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes. </p><br><p></p>

Finite Element Analysis of Human Tibia Modeled as a Functionally Graded Material

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Ashish Tiwari ◽  
Pankaj Wahi ◽  
Niraj Sinha
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Stress Distribution ◽  
Functionally Graded Material ◽  
Material Properties ◽  
Functionally Graded ◽  
Element Analysis ◽  
Cross Sectional ◽  
Human Tibia ◽  
Graded Material

Human tibia, the second largest bone in human body, is made of complex biological material having inhomogeneity and anisotropy in such a manner that makes it a functionally graded material. While analyses of human tibia assuming it to be made of different material regions have been attempted in past, functionally graded nature of the bone in the mechanical analysis has not been considered. This study highlights the importance of functional grading of material properties in capturing the correct stress distribution from the finite element analysis (FEA) of human tibia under static loading. Isotropic and orthotropic material properties of different regions of human tibia have been graded functionally in three different manners and assigned to the tibia model. The nonfunctionally graded and functionally graded models of tibia have been compared with each other. It was observed that the model in which functional grading was not performed, uneven distribution and unrealistic spikes of stresses occurred at the interfaces of different material regions. On the contrary, the models with functional grading were free from this potential artifact. Hence, our analysis suggests that functional grading is essential for predicting the actual distribution of stresses in the entire bone, which is important for biomechanical analysis. We find that orthotropic nature of the bone tends to increase the maximum von Mises stress in the entire tibia, while inclusion of cross-sectional inhomogeneity typically increases the stresses across normal cross section. Accordingly, our analysis suggests that both orthotropy as well as cross-sectional inhomogeneity should be included to correctly capture the stress distribution in the bone.

Sign in / Sign up

Export Citation Format

Share Document