scholarly journals A Versatile Punch Stroke Correction Model for Trial V-Bending of Sheet Metals Based on Data-Driven Method

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 4790
Author(s):  
Yongsen Yu ◽  
Zhiping Guan ◽  
Mingwen Ren ◽  
Jiawang Song ◽  
Pinkui Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Dimensional Analysis ◽  
Production Efficiency ◽  
Data Driven ◽  
Sheet Metals ◽  
Analysis Model ◽  
Correction Model ◽  
Air Bending ◽  
The Neural Network

During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending.

Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

2021 ◽  
Author(s):  
Daniel Roten ◽  
Kim B. Olsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Site Response ◽  
Site Amplification ◽  
Data Driven ◽  
Vertical Array ◽  
The Neural Network ◽  
Simplifying Assumptions ◽  
The Mean ◽  
Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

scholarly journals Data Driven Solutions and Discoveries in Mechanics Using Physics Informed Neural Network

2020 ◽  
Author(s):  
Qi Zhang ◽  
Yilin Chen ◽  
Ziyi Yang
Keyword(s):  
Neural Network ◽  
Differential Equations ◽  
Automatic Differentiation ◽  
Numerical Solutions ◽  
Data Driven ◽  
Attractive Alternative ◽  
The Finite Element Method ◽  
The Neural Network ◽  
Engineering Problems ◽  
Free Parameters

Deep learning has achieved remarkable success in diverse computer science applications, however, its use in other traditional engineering fields has emerged only recently. In this project, we solved several mechanics problems governed by differential equations, using physics informed neural networks (PINN). The PINN embeds the differential equations into the loss of the neural network using automatic differentiation. We present our developments in the context of solving two main classes of problems: data-driven solutions and data-driven discoveries, and we compare the results with either analytical solutions or numerical solutions using the finite element method. The remarkable achievements of the PINN model shown in this report suggest the bright prospect of the physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters. More broadly, this study shows that PINN provides an attractive alternative to solve traditional engineering problems.

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 ‘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>

The Poultry Meat Production Technical Efficiency Analysis Model Based on Intelligent Devices

2015 ◽  
Vol 713-715 ◽  
pp. 2181-2184
Author(s):  
Xiang Jie Niu ◽  
Hua Li
Keyword(s):  
Neural Network ◽  
Production Efficiency ◽  
Rbf Neural Network ◽  
Efficiency Analysis ◽  
Poultry Meat ◽  
Meat Production ◽  
Analysis Method ◽  
Analysis Model ◽  
Neural Network Algorithm ◽  
Intelligent Devices

The paper focuses on the poultry meat production efficiency analysis methods. In the poultry meat production procedure, the randomness is strong due to the various complex factors in the poultry meat production which will reduce the production efficiency. In order to avoid the defects of the traditional algorithms, the paper proposes the poultry meat production efficiency analysis method based on RBF neural network algorithm. The effect factors in the poultry meat production will be sifted as the basic data for production efficiency analysis. The RBF neural network model is built and the output results are used to analyze the poultry meat production efficiency with intelligent devices. The experiment results illustrate the improved algorithm can increase the poultry meat production efficiency.

scholarly journals Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yu Ding ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Kriging Model ◽  
Data Driven ◽  
Power Transformers ◽  
Global Approximation ◽  
The Neural Network ◽  
Modeling Accuracy ◽  
Diagnosis Method ◽  
Transformer Fault

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

Application of Artificial Neural Networks to Concrete Pavement Joint Evaluation

1996 ◽  
Vol 1540 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Anastasios M. Ioannides ◽  
Don R. Alexander ◽  
Michael I. Hammons ◽  
Craig M. Davis
Keyword(s):  
Neural Network ◽  
Dimensional Analysis ◽  
Load Transfer ◽  
A Priori ◽  
The United States ◽  
Concrete Pavement ◽  
Data Generation ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Joint Evaluation

Application of the principles of dimensional analysis has recently led to the development of a robust method for assessing the deflection and stress load transfer efficiencies of concrete pavement joints and for backcalculating joint parameters. The new method eliminates the need to make a priori assumptions since pertinent inputs can now be experimentally determined using the falling weight deflectometer. A data base has been generated using numerical integration of Westergaard-type integrals and has been used to train a backpropagation neural network algorithm for joint evaluation. The resulting computer program is simple, efficient, and precise and can be used on site for immediate results. Its predictions are verified by comparisons with closed-form and finite-element solutions pertaining to data collected at three major civilian airports in the United States, including the new Denver International Airport. Also discussed is the role of dimensional analysis in the generation of the training set for a neural network. It is demonstrated that significant savings can be achieved through reduction of the dimensionality of the problem, which could be reinvested in broadening the range of applicability of the neural network. Comparison of neural network predictions with those from conventional regression analysis and from direct interpolation illustrates the benefits of data generation on the basis of fundamental principles of mechanics.

scholarly journals Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Wen ◽  
Jun He
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Thoracic Surgery ◽  
Operating Room ◽  
Postoperative Infection ◽  
Operating Room Management ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Surgery Patient ◽  
The Neural Network

Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the effect of the operation and the subsequent recovery. Therefore, it is necessary to control the disease in time and adopt more scientific and comprehensive nursing measures. Based on the neural network algorithm, this paper constructs a neural network-based factor analysis model and applies the operating room management nursing to postoperative infection nursing after thoracic surgery and verifies the effect through the neural network model. The statistical parameters in this article mainly include the postoperative infection rate of thoracic surgery, patient satisfaction, postoperative rehabilitation effect, and complications. Through statistical analysis, it can be known that operating room management and nursing can play an important role in postoperative infection nursing after thoracic surgery, effectively reducing postoperative infection nursing after thoracic surgery, and improving the recovery effect of patients after infection.

scholarly journals Contributions towards Data driven Deep Learning methods to predict Steady State Fluid Flow in mechanical Heart Valves

2021 ◽  
Vol 7 (2) ◽  
pp. 625-628
Author(s):  
Jan Oldenburg ◽  
Julian Renkewitz ◽  
Michael Stiehm ◽  
Klaus-Peter Schmitz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Steady State ◽  
Flow Field ◽  
Heart Valves ◽  
Data Driven ◽  
High Shear ◽  
Blood Flows ◽  
The Neural Network ◽  
Data Driven Approach

Abstract It is commonly accepted that hemodynamic situation is related with cardiovascular diseases as well as clinical post-procedural outcome. In particular, aortic valve stenosis and insufficiency are associated with high shear flow and increased pressure loss. Furthermore, regurgitation, high shear stress and regions of stagnant blood flow are presumed to have an impact on clinical result. Therefore, flow field assessment to characterize the hemodynamic situation is necessary for device evaluation and further design optimization. In-vitro as well as in-silico fluid mechanics methods can be used to investigate the flow through prostheses. In-silico solutions are based on mathematical equitation’s which need to be solved numerically (Computational Fluid Dynamics - CFD). Fundamentally, the flow is physically described by Navier-Stokes. CFD often requires high computational cost resulting in long computation time. Techniques based on deep-learning are under research to overcome this problem. In this study, we applied a deep-learning strategy to estimate fluid flows during peak systolic steady-state blood flows through mechanical aortic valves with varying opening angles in randomly generated aortic root geometries. We used a data driven approach by running 3,500 two dimensional simulations (CFD). The simulation data serves as training data in a supervised deep learning framework based on convolutional neural networks analogous to the U-net architecture. We were able to successfully train the neural network using the supervised data driven approach. The results showing that it is feasible to use a neural network to estimate physiological flow fields in the vicinity of prosthetic heart valves (Validation error below 0.06), by only giving geometry data (Image) into the Network. The neural network generates flow field prediction in real time, which is more than 2500 times faster compared to CFD simulation. Accordingly, there is tremendous potential in the use of AIbased approaches predicting blood flows through heart valves on the basis of geometry data, especially in applications where fast fluid mechanic predictions are desired.

MULTISCALE MODELING OF THE EFFECTIVE THERMAL CONDUCTIVITY OF 2D WOVEN COMPOSITES BY MECHANICS OF STRUCTURE GENOME AND NEURAL NETWORKS

2021 ◽  
Author(s):  
XIN LIU ◽  
BO PENG ◽  
WENBIN YU
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Multiscale Modeling ◽  
Network Models ◽  
Thermal Design ◽  
Data Driven ◽  
Woven Composites ◽  
Neural Network Models ◽  
The Neural Network

A data-driven multiscale modeling approach is developed to predict the effective thermal conductivity of two-dimensional (2D) woven composites. First, a two-step homogenization approach based on mechanics of structure genome (MSG) is developed to predict effective thermal conductivity. The accuracy and efficiency of the MSG model are compared with the representative volume element (RVE) model based on three-dimensional (3D) finite element analysis (FEA). Then, the simulation data is generated by the MSG model to train neural network models to predict the effective thermal conductivity of three 2D woven composites. The neural network models have mixed input features: continuous input (e.g., fiber volume fraction and yarn geometries) and discrete input (e.g., weave patterns). Moreover, the neural network models are trained with the normalized features to enable reusability. The results show that the developed data-driven models provide an ultra-efficient yet accurate approach for the thermal design and analysis of 2D woven composites.

Sign in / Sign up

Export Citation Format

Share Document