Applicability of Artificial Neural Network to Estimate Sound Transmission Loss of Ultrafine Glass Fiber Felts

2019 ◽  
Vol 105 (4) ◽  
pp. 650-656 ◽  
Author(s):  
Fei Wang ◽  
Zhaofeng Chen ◽  
Cao Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Fiber ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Areal Density ◽  
Sound Frequency ◽  
Sound Transmission Loss ◽  
Ann Model ◽  
Artificial Neural

In the present study, the sound transmission loss (STL) of ultrafine glass fiber felts in terms of areal density and sound frequency has been modeled by artificial neural network (ANN), the Law of Theoretic Mass and fitting polynomial, respectively. The STL of ultrafine glass fiber felts with the areal density ranging from 0 to 300 g/m2 and at the sound frequency ranging from 500 to 6300 Hz was employed as training data for ANN. By the optimization of ANN structure, the number of neurons in the two hidden layers was determined to 8 and 4 respectively. The mean squared error of the ANN model was only 0.191 and the correlation coefficient was 0.9989, which showed high accuracy for estimating the STL of the felts. Compared with other two models, the ANN model showed excellent agreement with the measured results and it's very appropriate for the estimation of acoustic properties of ultrafine glass fiber felts.

Application of Artificial Neural Network for the Prediction of Surface Roughness in Drilling GFRP Composites

2013 ◽  
Vol 766 ◽  
pp. 21-36 ◽  
Author(s):  
K. Palanikumar ◽  
B. Latha ◽  
V.S. Senthilkumar ◽  
J. Paulo Davim
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Composite Materials ◽  
Glass Fiber ◽  
Fiber Reinforced ◽  
Ann Model ◽  
Gfrp Composites ◽  
Glass Fiber Reinforced ◽  
Artificial Neural

Composite materials are used in different fields, due to their excellent properties. Glass fiber reinforced composite materials are used in aerospace, automobile, sport goods, etc. Joining by drilling operation is necessary for this composite to perform assembly. Surface roughness of the holes plays an important role in mechanical joints. Good surface leads to the precision fits and efficient joints. The present article discusses the use of artificial neural network (ANN) for the prediction of surface roughness in drilling glass fiber reinforced plastic (GFRP) composites. The experiments are carried out on computer numeric control machining center. The results indicated that the well-trained ANN model could able to predict the surface roughness in drilling of GFRP composites.

Predicting the Air Permeability of Ultrafine Glass Fiber Felts: A Comparison of Artificial Neural Network, Linear Fitting, and Polynomial Fitting

2021 ◽  
Vol 8 (2) ◽  
pp. 9-17
Author(s):  
Fei Wang ◽  
Zhaofeng Chen ◽  
Cao Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Fiber ◽  
Mean Squared Error ◽  
Air Permeability ◽  
Ann Model ◽  
Polynomial Fitting ◽  
Linear Fitting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, the air permeability of ultrafine glass fiber felts (UGFFs) as a function of bulk density and thickness was predicted by three analysis methods including linear fitting, polynomial fitting, and an artificial neural network (ANN). A 36-set database was obtained by the measurements of samples produced by the flame blowing process. It was shown that the ANN structure with six neurons in the hidden layer was optimal. The ANN model showed much better quality of predicting the permeation rate compared with linear fitting and polynomial fitting, which was evaluated by three important parameters, namely mean relative error (MRE), mean squared error (MSE), and correlation coefficient (R). The prediction diagrams applying the ANN model also matched the theoretical analysis very well, which verified the advantages and practicability of ANN.

Sound insulation of multi-layer glass-fiber felts: Role of morphology

2016 ◽  
Vol 87 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Yong Yang ◽  
Binbin Li ◽  
Zhaofeng Chen ◽  
Ni Sui ◽  
Zhou Chen ◽  
...  
Keyword(s):  
Glass Fiber ◽  
Transmission Loss ◽  
Resonance Effect ◽  
Sound Transmission ◽  
Areal Density ◽  
Sound Insulation ◽  
Sound Transmission Loss ◽  
Layer Structures ◽  
Air Layers

Glass-fiber felts have emerged as a popular material for noise reduction. This paper investigates the effect of various morphologies (micro-layer, macro-layer and air-layer) of glass-fiber felts on sound insulation. The sound transmission loss is measured by a Brüel & Kjár (B&K) impedance tube. The results show that the sound insulation of glass-fiber felts can be improved by increasing the number of macro-layers. The comparison between the macro- and micro-layer of glass-fiber felts on sound insulation is systematically carried out. Notably, the sound transmission loss of glass-fiber felts with similar areal density and thickness favors macro-layer structures over micro-layer structures. A simple model is established to explain this phenomenon. In addition, the sound transmission loss exhibits period fluctuations due to the presence of the air-layer between glass-fiber felts, which can be theoretically explained by the resonance effect. It is found that sound transmission loss can be improved by increasing the number of air-layers.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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