Optimum design based on mathematical model and neural network to predict weld parameters for fillet joints

1997 ◽  
Vol 16 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Hyeong-Soon Moon ◽  
Suck-Joo Na
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Optimum Design ◽  
Weld Parameters

Application of Neural Network and Genetic Algorithm to the Optimum Design of Perforated Tube Mufflers

2009 ◽  
Vol 25 (3) ◽  
pp. N7-N16 ◽  
Author(s):  
M.-C. Chiu ◽  
Y.-C. Chang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimum Design ◽  
Transmission Loss ◽  
Research Work ◽  
Sound Transmission ◽  
Optimum Process ◽  
Sound Transmission Loss

AbstractResearch on new techniques of perforated silencers has been well addressed. However, the research work on shape optimization for a volume-constrained silencer within a constrained machine room is rare. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to simplify the optimum process, a simplified mathematical model of the muffler is constructed with a neural network using a series of input design data (muffle dimensions) and output data (theoretical sound transmission loss) obtained by a theoretical mathematical model (TMM). To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to pure tones (500, 1000, 2000Hz) are exemplified and discussed.Before the GA operation can be carried out, the accuracy of the TMM is checked by Crocker's experimental data. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM confirm.The results reveal that the maximum value of the sound transmission loss (STL) can be optimally obtained at the desired frequencies. Consequently, it is obvious that the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers.

Optimum design of perforated plug mufflers using a neural network and a genetic algorithm

2008 ◽  
Vol 223 (4) ◽  
pp. 935-952 ◽  
Author(s):  
Y-C Chang ◽  
M-C Chiu ◽  
M-M Cheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Noise Reduction ◽  
Optimum Design ◽  
Transmission Loss ◽  
Real Data ◽  
Wave Theory ◽  
Optimum Process ◽  
Design Data

Research on new techniques of perforated plug silencers has been well addressed. Most researchers have explored noise reduction effects based on a pure plane wave theory. However, the maximum noise reduction of a silencer under a space constraint, which frequently occurs in engineering problems, is rarely addressed. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to save the design time during the flexible optimum process, a simplified mathematical model of a muffler is constructed with a neural network with a series of real data — input design data (muffle dimensions) and output data (theoretical sound transmission loss (STL)) were approximated by a theoretical mathematical model (TMM) in advance. To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to various parameter sets and pure tones (500, 1000, and 2000 Hz) are exemplified and discussed. Before the GA operation is carried out, the approximation between TMM and real data is checked. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM conform. Optimal results reveal that the maximum amount of the STL can be optimally obtained at the desired frequencies. Consequently, the optimum algorithm proposed in this study can provide an efficient method to develop optimal silencers in industry.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

scholarly journals Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

2021 ◽  
pp. 70-74
Author(s):  
Iulia Clitan ◽  
◽  
Adela Puscasiu ◽  
Vlad Muresan ◽  
Mihaela Ligia Unguresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Web Application ◽  
Point Of View ◽  
The Novel ◽  
End User ◽  
First Case ◽  
Novel Coronavirus ◽  
Second Wave ◽  
Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

scholarly journals Identification of Neural Network Model of Robot to Solve the Optimal Control Problem

2021 ◽  
Vol 20 (6) ◽  
pp. 1254-1278
Author(s):  
Elizaveta Shmalko ◽  
Yuri Rumyantsev ◽  
Ruslan Baynazarov ◽  
Konstantin Yamshanov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mathematical Model ◽  
Optimal Control ◽  
Neural Network Model ◽  
Software Package ◽  
Control Object ◽  
Robotic Systems ◽  
Object Model ◽  
Modern Machine

To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.

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