scholarly journals An Artificial Neural Network Approach to Plastic Collapse of Oval Boiler Tubes

2008 ◽  
Vol 41-42 ◽  
pp. 421-426 ◽  
Author(s):  
K. Zarrabi ◽  
A. Basu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plastic Collapse ◽  
Element Analysis ◽  
Neural Network Approach ◽  
Collapse Pressure ◽  
Thin Tube ◽  
Processing Plants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Boilers in power, refinery and chemical processing plants contain extensive range of tube bends. Tube bends are manufactured by bending a straight-section tube. As a result, the crosssection of a tube bend becomes oval. Using the finite element analysis (FEA) and artificial neural network (ANN), the paper presents the relationships between the plastic collapse pressures and tube bend dimensions with various degrees of ovality. It is found that as ovality increases the plastic collapse pressure decreases. Also, the reduction of plastic collapse pressure with ovality is small for a thick tube bend when compared with that for a thin tube bend.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

Prediction of Wear Behavior in Porous Sintered Steels: Artificial Neural Network Approach

2018 ◽  
Vol 18 (2) ◽  
pp. 111-115
Author(s):  
Hassan Abdoos ◽  
Ahmad Tayebi ◽  
Meysam Bayat
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Wear Resistance ◽  
Wear Behavior ◽  
Affecting Factors ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Comparison Of The Results

Abstract Due to the increasing usage of powder metallurgy (PM), there is a demand to evaluate and improve the mechanical properties of PM parts. One of the most important mechanical properties is wear behavior, especially in parts that are in contact with each other. Therefore, the choice of materials and select manufacturing parameters are very important to achieve proper wear behavior. So, prediction of wear resistance is important in PM parts. In this paper, we try to investigate and predict the wear resistance (volume loss) of PM porous steels according to the affecting factors such as: density, force and sliding distance by artificial neural network (ANN). ANN training was done by a multilayer perceptron procedure. The comparison of the results estimated by the ANN with the experimental data shows their proper matching. This issue confirms the efficiency of using method for prediction of wear resistance in PM steel parts.

AN ARTIFICIAL NEURAL NETWORK APPROACH TO HOTEL EMPLOYEE SATISFACTION: THE CASE OF CHINA

2008 ◽  
Vol 36 (4) ◽  
pp. 467-482 ◽  
Author(s):  
Xizhou Tian ◽  
Yongjian Pu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Employee Satisfaction ◽  
High Rate ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Hotel Employee ◽  
And Gender ◽  
Artificial Neural Network Ann

At present, the hotel employment sector in China has a high rate of employee turnover compared to other services. This is not unlike other countries. The reason for the turnover among hotel employees may be lower worker satisfaction resulting in decreased – or no – loyalty to employers. This study was based on an Artificial Neural Network (ANN). The factors influencing employee satisfaction were examined and the impacts of demographic characteristics on hotel employee satisfaction were analyzed. Results show that hotel employee satisfaction in China is low, hotel employee satisfaction differs by age and gender, and that professional development opportunities for employees and the long-term growth prospects of the hotels themselves are the most important contributors to employee satisfaction. On the basis of these findings, several recommendations for improving employee satisfaction, thereby sustaining the long-term economic health of China's hospitality industry, are provided.

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

An Artificial Neural Network Approach to Cooling Analysis of Electronic Components in Enclosures Filled With Nanofluids

2011 ◽  
Vol 133 (1) ◽  
Author(s):  
A. Kargar ◽  
B. Ghasemi ◽  
S. M. Aminossadati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cfd Simulation ◽  
Electronic Components ◽  
Neural Network Approach ◽  
Cooling Performance ◽  
Computational Fluid Dynamics Cfd ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Lower Temperature

Computational fluid dynamics (CFD) and artificial neural network (ANN) are used to examine the cooling performance of two electronic components in an enclosure filled with a Cu-water nanofluid. The heat transfer within the enclosure is due to laminar natural convection between the heated electronic components mounted on the left and right vertical walls with a relatively lower temperature. The results of a CFD simulation are used to train and validate a series of ANN architectures, which are developed to quickly and accurately carry out this analysis. A comparison study between the results from the CFD simulation and the ANN analysis indicates that the ANN accurately predicts the cooling performance of electronic components within the given range of data.

scholarly journals Application of artificial neural network for design: a case of paper helicopter

2018 ◽  
Vol 192 ◽  
pp. 01044
Author(s):  
Tossapol Kiatcharoenpol ◽  
Tanaporn Klangpetch
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Iteration Process ◽  
Testing Procedure ◽  
Depth Of Cut ◽  
Neural Network Approach ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Target Output

The design engineering is one of essential work in modern manufacturing environment. The optimization is principal technique to be used widely for searching the solution. However, primary process of optimization is to know the relation between design input parameters and target output. In this work, an artificial neural network (ANN) approach as an intelligent algorithm is proposed to construct the relation and also provides it in form of mathematic modeling. Even though the ANN modeling is so call a backblock due to difficulty to understand complicated equations, it is simply constructed by automate iteration process. A case of paper helicopter is used as an example of the application. The classical 2k Factorial design is used to provide an experiment plan to create training and testing data. 93 experiments are carried out. The architecture of ANN is set according to lowest Mean square error (MSE) of training and testing procedure. The result of 5-10-1 architecture has shown ability to accurately predict output, landing time, with MSE of 0.012. With such a highly quantitative accuracy of results, the developed model using the neural network approach can be used for finding the suitable input parameters to achieve a desired target output. In this case, the design of dimension (A) Depth of cut wing is 1.3 cm., (B) Length of wing is 12.9 cm., (C) Length of body is 9.0, (D) Width of body is 2.0 cm., and (E) Depth of cut body is 0 cm. yield the lowest area of a paper helicopter that can meet the target landing time, 2.85 + 5% second.

An Artificial Neural Network Approach to Fatigue Crack Growth

2011 ◽  
Vol 275 ◽  
pp. 3-6
Author(s):  
K. Zarrabi ◽  
W.W. Lu ◽  
A.K. Hellier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Crack Length ◽  
Neural Network Approach ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Amplitude Mode ◽  
Artificial Neural Network Approach

This paper proposes a new three-layer artificial neural network (ANN) to predict the fatigue crack length under constant amplitude mode I cyclic loading. It is shown that the proposed model predicts the crack length with an error of less than 0.05%, and more accurately than the current commonly-used models.

scholarly journals Characterization of Superplastic Deformation Behavior for a Novel Al-Mg-Fe-Ni-Zr-Sc Alloy: Arrhenius-Based Modeling and Artificial Neural Network Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 2208
Author(s):  
Ahmed O. Mosleh ◽  
Anton D. Kotov ◽  
Anna A. Kishchik ◽  
Oleg V. Rofman ◽  
Anastasia V. Mikhaylovskaya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Superplastic Forming ◽  
Equation Model ◽  
The Novel ◽  
Neural Network Approach ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The application of superplastic forming for complex components manufacturing is attractive for automotive and aircraft industries and has been of great interest in recent years. The current analytical modeling theories are far from perfect in this area, and the results deduced from it characterize the forming conditions insufficiently well; therefore, successful numerical modeling is essential. In this study, the superplastic behavior of the novel Al-Mg-Fe-Ni-Zr-Sc alloy with high-strain-rate superplasticity was modeled. An Arrhenius-type constitutive hyperbolic-sine equation model (ACE) and an artificial neural network (ANN) were developed. A comparative study between the constructed models was performed based on statistical errors. A cross validation approach was utilized to evaluate the predictability of the developed models. The results revealed that the ACE and ANN models demonstrated strong workability in predicting the investigated alloy’s flow stress, whereas the ACE approach exhibited better predictability than the ANN.

Sign in / Sign up

Export Citation Format

Share Document