scholarly journals Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3170
Author(s):  
Vinayak Kallannavar ◽  
Subhaschandra Kattimani ◽  
Manzoore Elahi M. Soudagar ◽  
M. A. Mujtaba ◽  
Saad Alshahrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Model ◽  
Prediction Model ◽  
Stiffness Matrix ◽  
Laminated Composite ◽  
Core Material ◽  
Linear Strain ◽  
Influence Of Temperature ◽  
Composite Sandwich ◽  
The Impact

The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

Generalized Regression Neural Network Cargo Flow Forecast Model in Logistics Park Based on Radial Basis Function

2011 ◽  
Vol 287-290 ◽  
pp. 622-625
Author(s):  
Qin Zhang ◽  
Yin Kui Liang ◽  
Wen Xia Yu ◽  
Qiang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Forecast Model ◽  
Scientific Basis ◽  
Generalized Regression Neural Network ◽  
Chain Management ◽  
Logistics Park ◽  
Generalized Regression ◽  
The Impact ◽  
Flow Forecast

According to cargo flow, the strength of the logistics supply need could be predicted. Improving predicting accuracy can provide a scientific basis for the construction and operation on the logistics park. Generalized regression neural network model of logistics park is introduced under the impact of supply chain management, and designing steps about the prediction model is given. And the prediction model predicts Jinan Gaijiagou Logistics Park well.

scholarly journals Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 11
Author(s):  
Qingwu Fan ◽  
Li Shuo ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Design Stage ◽  
Cooling Load ◽  
Improve Energy Efficiency ◽  
Heating And Cooling ◽  
Heating And Cooling Load ◽  
Ipso Algorithm ◽  
The Impact

Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman’s correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.

scholarly journals BENDING AFTER IMPACTKOMPOSIT SANDWICH BERPENGUAT SERAT BAMBU-FIBERGLASS DENGAN COREPOLYURETHANE RIGID FOAM

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Agus Dwi Catur ◽  
S. Sinarep ◽  
Paryanto Dwi Setyawan ◽  
Achmad Zainuri ◽  
S. Supriadi
Keyword(s):  
Impact Energy ◽  
Bending Strength ◽  
Laminated Composite ◽  
Rigid Foam ◽  
Composite Sandwich ◽  
Drop Weight ◽  
Reinforcing Fibers ◽  
Weight Impact ◽  
Glass Reinforcement ◽  
The Impact

Impact of the composite sandwich made of materials that become defect, how defects after impact  in material need to be examined .Is that defected composites and wich still has the strength toremain in use?, then the bending after impact strength testing must be done to answer these questions.Composite sandwich with bamboo fiber-fiber glass reinforcement and with a core of 25mm rigid polyurethane foam sheet was produced. Composite sandwich made with two composite sandwich laminate as skinflanking the core. Laminated composite sandwich in this study varied number of layers of reinforcing fibers and type of fiber. Specimens subjected to drop weight impact with varying energy then defect researched and bending  after impact strengthtested. Defects formed on the composite experiencing a drop weight impact loads are: delamination, basin and through hole. The more layers of reinforcing fibers in the composite skin causing moreshallowbasinformed by theresidualimpact. The greater the impact energy causes greater depth of residual basin. Composite sandwich still has the bending strength after impact. Residual bending strength decreases with increasing impact energy imposed on the composite.

scholarly journals Energy absorbing composite structure for frontal pedestrian protection in electric light vehicles

2017 ◽  
Vol 9 (2) ◽  
pp. 168781401668796 ◽  
Author(s):  
David Valladares ◽  
Juan J Alba ◽  
Ines Altubo
Keyword(s):  
Numerical Model ◽  
Composite Structure ◽  
Sandwich Composite ◽  
Pedestrian Protection ◽  
The Finite Element Method ◽  
Composite Sandwich ◽  
Real Performance ◽  
Electric Light ◽  
Energy Absorbing ◽  
The Impact

This article collects the development of a frontal composite structure for electric light vehicles (concretely within L7e European category), which has been required to fulfil energy absorption capabilities for pedestrian protection. An initial design made of a composite sandwich structure (glass fibre skins with polyvinyl chloride foam core) was proposed and a prototype was manufactured and tested against impact. Then, a numerical model was created and the impact test was simulated by the finite element method. After adjusting the numerical model to the real performance of the component, the initial material configuration of the sandwich composite was optimized according to design objectives involving safety, current regulations and repairability.

Quasi-Static Failure Analysis of Composite Sandwich Panels

2011 ◽  
Vol 121-126 ◽  
pp. 316-319
Author(s):  
Ivan Yao Hsu
Keyword(s):  
Finite Element ◽  
Failure Analysis ◽  
Sandwich Panels ◽  
Low Velocity Impact ◽  
Core Material ◽  
Low Velocity ◽  
Velocity Impact ◽  
Composite Sandwich ◽  
The Impact ◽  
Static Failure

Due to many of advantages such as lightweighted, high-bending mechanical characteristics, composite sandwich materials play an important role in today’s structural manufacturing industry. This study is to aim at the low-velocity impact responses of sandwich plates subjected to a rigid, spherical-shaped impactor. The sandwich made of PVC core material (two kinds of core with different densities) and FRP facesheets (three kinds of fibrous laminae) were investigated experimentally and numerically. Because the dynamic behaviors of specimens due to low velocity impact is nearly the same to those in static indentation, the impact failure analysis of sandwich material can be simulated statically. Beside the experiment, finite element method was employed to analyze the static failure behaviors of sandwich panels. With the maximum stress failure criterion as well as a modified stiffness degradation method coded in the finite element software, the initial failure and sequential progressive failure process can be analyzed effectively.

Research on the Impact of Climate Cactors on Wood Width Based on the Improved RBF Model

2012 ◽  
Vol 461 ◽  
pp. 717-720
Author(s):  
Qi Yue ◽  
Zhuo Ran Lv ◽  
Xue Mei Guan
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Prediction Model ◽  
Physical Properties ◽  
Rbf Neural Network ◽  
Wood Formation ◽  
Ground Temperature ◽  
Climate Factors ◽  
Poplar Plantation ◽  
The Impact

Choosing cathay poplar as research object ,with the aim to reveal that how the climate change may affect the wood formation, by using the improved RBF neural network the paper studies the affects of the climate factors such as temperature, sunhine, rainfall and ground temperature on the physical properties of cathay poplar plantation and establish a prediction model finally. The simulation was carried out and the results shows that the error is less than 2.8%.

Numerical Analysis on Failure Behavior of Composite Sandwich Plate

2013 ◽  
Vol 284-287 ◽  
pp. 178-182
Author(s):  
Yao Hsu
Keyword(s):  
Finite Element ◽  
Failure Mechanism ◽  
Sandwich Plate ◽  
Core Material ◽  
Structural Safety ◽  
Failure Behavior ◽  
Sandwich Plates ◽  
Composite Sandwich ◽  
Finite Element Software ◽  
The Impact

The Composite sandwich plate is made of two laminated face-sheets and one core material. Since such a kind of structure has many advantages, they have been widely used in structural manufacturing industry. However, when sandwich plates are impacted by transverse loadings, damages that are usually invisible would occur inside the sandwich plate and those damages would potentially reduce the structural safety. Therefore, it is necessary to elucidate the failure mechanism and how they affect the failure behaviors of sandwich structures for safety purpose. To this end, the present study is to investigate the impact failure behaviors of sandwich plates subjected to a rigid spherical impactor. Numerical simulation approach is carried out by finite element method. To predict the initial failure, several failure criteria to face-sheets and core material are proposed. In addition, to further simulate the progressive failure behaviors, a stiffness modification method is proposed and incorporated into the finite element software. The analytical results show that the local failure including fiber breakages, delamination, core cracking and plasticity is the main failure mechanism of cases studied. Furthermore, parametric study is also conducted and discussed in the paper.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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