scholarly journals Using Artificial Neural Networks to Determine Wear of Composite Friction Material

2021 ◽  
Vol 20 (4) ◽  
pp. 345-351
Author(s):  
A. V. Liashok ◽  
Yu. B. Popova
Keyword(s):  
Neural Networks ◽  
Wear Resistance ◽  
Artificial Neural Networks ◽  
Service Life ◽  
Network Architecture ◽  
Friction Material ◽  
Operational Parameters ◽  
Friction Materials ◽  
Operating Modes ◽  
Artificial Neural

Sintered friction materials are widely used in friction units of automotive vehicles and special purpose vehicles.  The main purpose is to transmit torque to the actuator. The development of the technology market requires the development and use of new units. At the same time, the creation of new materials is required, which also applies to sintered friction materials. This group of materials is characterized by a high service life, efficiency of torque transmission, as well as the ability to restore performance in case of violation of operating modes. One of the most significant parameters characterizing  a sintered friction material is wear resistance. In most cases, it determines not only the resource of the unit itself, but the entire machine as a whole. A special place is occupied by brake units, which also use friction materials. The increased wear  resistance of the friction material contributes to a decrease in the efficiency and service life of the brake system. Evaluation  of the wear resistance of a friction material for the given operational parameters is a very long and costly process. The development of methodology and methods for accelerating the assessment of wear resistance is an important scientific and practical task. The paper presents the results of using artificial neural networks to predict the service life of a composite friction material based on copper on the sliding speed, pressure on the material and the amount of lubricant supplied to the friction zone. An artificial neural network has been trained using an array of experimental data for the FM-15 friction material.  The training results have shown high accuracy, correctness of the proposed and implemented network architecture. The developed software has demonstrated its efficiency and the possibility of using it in calculations to determine the wear of a composite friction material.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

scholarly journals Jaringan Syaraf Tiruan dalam Memprediksi Penjualan Air Minum dalam Kemasan dengan Metode Backpropogation

2019 ◽  
Vol 1 ◽  
pp. 512
Author(s):  
Hafiz Pratama ◽  
Poningsih Poningsih ◽  
Jalaluddin Jalaluddin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Bottled Water ◽  
Accuracy Rate ◽  
Matlab Software ◽  
Artificial Neural

This study predicts the sale of bottled water by applying Artificial Neural Networks. The application uses the Backpropogation Algorithm where the data entered is the number of sales, then Artificial Neural Networks are formed by determining the number of each layer. After the network is formed training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, output units and network architecture. Testing is done with Matlab software. Predictions with the best accuracy use 3-10-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using architecture 3-40-1 with an accuracy rate of 33%.

scholarly journals Structural Features for Furan-Derived Fruity and Meaty Aroma Impressions

2016 ◽  
Vol 11 (10) ◽  
pp. 1934578X1601101
Author(s):  
Bettina Wailzer ◽  
Johanna Klocker ◽  
Peter Wolschann ◽  
Gerhard Buchbauer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Structural Features ◽  
Classification Rule ◽  
Classification Rate ◽  
Furan Derivatives ◽  
Information Index ◽  
The Neural Network ◽  
Artificial Neural

Furan derivatives are part of nearly all food aromas. They are mainly formed by thermal degradation of carbohydrates and ascorbic acid and from sugar-amino acid interactions during food processing. Caramel-like, sweet, fruity, nutty, meaty, and burnt odor impressions are associated with this class of compounds. In the presented work, structure-activity relationship (SAR) investigations are performed on a series of furan derivatives in order to find structural subunits, which are responsible for the particular characteristic flavors. Therefore, artificial neural networks are applied on a set of 35 furans with the aroma categories “meaty” or “fruity” to calculate a classification rule and class boundaries for these two aroma impressions. By training a multilayer perceptron network architecture with a backpropagation algorithm, a correct classification rate of 100% is obtained. The neural network is able to distinguish between the two studied groups by using the following significant descriptors as inputs: number of sulfur atoms, Looping Centric Information Index, Folding Degree Index and Petitjean Shape Indices. Finally, the results clearly demonstrate that artificial neural networks are successful tools to investigate non-linear qualitative structure-odor relationships of aroma compounds.

Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ahmed K. Abbas ◽  
Salih Rushdi ◽  
Mortadha Alsaba ◽  
Mohammed F. Al Dushaishi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Complex Problem ◽  
Real Field ◽  
Operational Parameters ◽  
Rate Of Penetration ◽  
Weight On Bit ◽  
Artificial Neural ◽  
Deviated Wells

Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.

FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

2004 ◽  
Vol 03 (01) ◽  
pp. 145-165 ◽  
Author(s):  
WEI HUANG ◽  
K. K. LAI ◽  
Y. NAKAMORI ◽  
SHOUYANG WANG
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Network Architecture ◽  
Future Research ◽  
Promising Alternative ◽  
Practical Applications ◽  
Future Research Directions ◽  
Artificial Neural ◽  
Input Variables

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

scholarly journals EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol XLII-5 ◽  
pp. 823-827 ◽  
Author(s):  
A. Bhatia ◽  
S. Pasari ◽  
A. Mehta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Short Term Memory ◽  
Economic Loss ◽  
Earthquake Forecasting ◽  
Accuracy Evaluation ◽  
Neural Network Architecture ◽  
Forecasting System ◽  
Artificial Neural

<p><strong>Abstract.</strong> Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences.</p>

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

ANALYSIS OF DOCUMENTS RANKING ALGORITHMS BASED ON THE ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Author(s):  
Andrew Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Search Algorithm ◽  
Network Algorithms ◽  
Text Document ◽  
Ranking Algorithms ◽  
Selection For ◽  
Artificial Neural

The object of this study is to analyse the effectiveness of document ran­ king algorithms in search engines that use artificial neural networks to match the texts. The purpose of the study was to inspect a neural network model of text document ran­ king that uses clustering, factor analysis, and multi-layered network architecture. The work of neural network algorithms was compared with the standard statistical search algorithm OkapiBM25. The result of the study is to evaluate the effectiveness of the use of particular models and to recommend model selection for specific datasets.

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