Predicting Model in the Smelting Magnesium by Silicon-Thermo-Reduction Based on Artificial Neural Network

2010 ◽  
Vol 146-147 ◽  
pp. 571-574
Author(s):  
Liang Bo Ji ◽  
Yong Zhi Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Effective Means ◽  
Limited Liability ◽  
Predicting Model ◽  
Artificial Neural ◽  
Set Up

This paper described the application of neural networks in predicting the rate of producing magnesium by silicon-thermo-reduction. Fir st of all, a mathematical model between the process parameters and the the rate of producing magnesium was set up with neural network. When the model was satisfied, it could be used for predicting the rate of producing magnesium. Through doing a great number of productive tests in the winca(hebi) magnesium company with limited liability according to the satisfied model, the rate of the producing magnesium is increasing obviously. So it is a kind of effective means for increasing producing magnesium by silicon-thermo-reduction.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

Relative density prognosis for directed energy deposition with the help of artificial neural networks

2021 ◽  
Vol 63 (1) ◽  
pp. 41-47
Author(s):  
Angelina Marko ◽  
Andreas Schafner ◽  
Julius Raute ◽  
Michael Rethmeier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Relative Density ◽  
Process Parameters ◽  
Energy Deposition ◽  
Data Sets ◽  
Artificial Neural ◽  
Directed Energy

Abstract Additive manufacturing, and therefore directed energy deposition, is gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes. First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parameters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %.

A Mathematical Model for Wire Cut Electrical Discharge Machine Parameters Using Artificial Neural Network

2014 ◽  
Vol 984-985 ◽  
pp. 9-14
Author(s):  
G. Sankara Narayanan ◽  
Durairaj Vasudevan
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Electric Discharge ◽  
Electrical Discharge Machine ◽  
Machining Process ◽  
Electric Discharge Machine ◽  
Discharge Machine ◽  
Artificial Neural

Unconventional machining process finds heavy application in aerospace, automobile and in production industries where accuracy is most needed. This process is chosen over other traditional methods because of the advent of composite and high strength materials, multifaceted parts and also because of its elevated precision. Regularly in unconventional machines, trial and error method is used to fix the values of process parameters. An algorithm incorporating Artificial Neural Network (ANN) is proposed to create mathematical model functionally relating process parameters and operating parameters of a wire cut electric discharge machine (WEDM) and copper is the work piece. This is accomplished by training a learning algorithm of feed forward neural network with back propagation. The required data used for training and testing the ANN is obtained by conducting trial runs in wire cut electric discharge machine. Proposed algorithm paves reduction in time for fixing the values for the process parameters and thus reduces the production time along with reduction in cost of machining processes and thereby increases the production as well as the efficiency. The programs for training and testing the neural network are developed, using matlab 7.0.1 package.

scholarly journals Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5127
Author(s):  
Szymon Buchaniec ◽  
Marek Gnatowski ◽  
Grzegorz Brus
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Box Model ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
The Mathematical Model

One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of existing mathematical models include different empirical parameters, and both approaches inherit inevitable experimental errors. Simultaneously, the approximation of neural networks can reproduce the solution exceptionally well if fed sufficient data. The difference is that an artificial neural network requires big data to build its accurate approximation, whereas a typical mathematical model needs several data points to estimate an empirical constant. Therefore, the common problem that developers meet is the inaccuracy of mathematical models and artificial neural networks. Another common challenge is the mathematical models’ computational complexity or lack of data for a sufficient precision of the artificial neural networks. Here we analyze a grey-box solution in which an artificial neural network predicts just a part of the mathematical model, and its weights are adjusted based on the mathematical model’s output using the evolutionary approach to avoid overfitting. The performance of the grey-box model is statistically compared to a Dense Neural Network on benchmarking functions. With the use of Shaffer procedure, it was shown that the grey-box approach performs exceptionally well when the overall complexity of a problem is properly distributed with the mathematical model and the Artificial Neural Network. The obtained calculation results indicate that such an approach could increase precision and limit the dataset required for learning. To show the applicability of the presented approach, it was employed in modeling of the electrochemical reaction in the Solid Oxide Fuel Cell’s anode. Implementation of a grey-box model improved the prediction in comparison to the typically used methodology.

scholarly journals Research on Application of Artificial Neural Network in Fault Diagnosis of Chemical Process

2021 ◽  
pp. 90-97
Author(s):  
Haonan Wang ◽  
Yijia Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Chemical Process ◽  
Chemical Processes ◽  
Artificial Neural ◽  
Process Fault Diagnosis

Chemical processes are usually toxic, corrosive, flammable and explosive. If the process fails, the danger is extremely high. Traditional model-based fault diagnosis methods need to establish an accurate mathematical model of the system, while modern engineering processes are usually large in scale and complex, and it is difficult to establish an accurate mathematical model. Artificial neural network has been widely used in chemical process because of its advantages of parallel processing, self-adaptation, robustness, learnability and fault tolerance. Artificial neural networks based on "deep learning" have been successfully applied to fault diagnosis in various chemical processes. This article summarizes the principle and development process of artificial neural networks, and analyzes the research progress and application status of deep neural networks in chemical process fault diagnosis based on cases. Finally, it is pointed out that deep neural network in the field of chemical process fault diagnosis is of great significance in solving the impact of less fault data and system state changes on the fault detection rate, and promoting the industrial application of fault diagnosis models.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

2021 ◽  
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Single Point ◽  
Tool Material ◽  
Deformation Mode ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Tool Shape ◽  
Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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