Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting
In the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real time updated accordi...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2015-03-01
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| Series: | Archives of Foundry Engineering |
| Subjects: | |
| Online Access: | http://www.degruyter.com/view/j/afe.2015.15.issue-1/afe-2015-0022/afe-2015-0022.xml?format=INT |
| _version_ | 1797720272475783168 |
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| author | Wróbel J. Kulawik A. Bokota A. |
| author_facet | Wróbel J. Kulawik A. Bokota A. |
| author_sort | Wróbel J. |
| collection | DOAJ |
| description | In the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real time updated according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source (Δz = 0). The main task of the model is control the assumed values of temperature at constant parameters of the heat source such as radius and power. Therefore the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The input data of the network are the values of temperature and the radius of the heated element. The learning, testing and validation sets were determined by using the equation of steady heat transfer process with a convective term. To verify the possibilities of the presented algorithm, based on the solve of the unsteady heat conduction with finite element method, a numerical simulation is performed. The calculations confirm the effectiveness of use of the presented solution, in order to obtain for example the constant depth of the heat affected zone for the geometrically variable hardened axisymmetric objects |
| first_indexed | 2024-03-12T09:16:59Z |
| format | Article |
| id | doaj.art-6b4c97c3d40d4bab8bb6cfd95cf4fbac |
| institution | Directory Open Access Journal |
| issn | 2299-2944 |
| language | English |
| last_indexed | 2024-03-12T09:16:59Z |
| publishDate | 2015-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Foundry Engineering |
| spelling | doaj.art-6b4c97c3d40d4bab8bb6cfd95cf4fbac2023-09-02T14:46:07ZengPolish Academy of SciencesArchives of Foundry Engineering2299-29442015-03-0115111912410.1515/afe-2015-0022afe-2015-0022Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of CastingWróbel J.0Kulawik A.1Bokota A.2Institute of Computer and Information Sciences, Czestochowa University of Technology, Dąbrowskiego 73, 42-200 Częstochowa, PolandInstitute of Computer and Information Sciences, Czestochowa University of Technology, Dąbrowskiego 73, 42-200 Częstochowa, PolandInstitute of Computer and Information Sciences, Czestochowa University of Technology, Dąbrowskiego 73, 42-200 Częstochowa, PolandIn the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real time updated according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source (Δz = 0). The main task of the model is control the assumed values of temperature at constant parameters of the heat source such as radius and power. Therefore the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The input data of the network are the values of temperature and the radius of the heated element. The learning, testing and validation sets were determined by using the equation of steady heat transfer process with a convective term. To verify the possibilities of the presented algorithm, based on the solve of the unsteady heat conduction with finite element method, a numerical simulation is performed. The calculations confirm the effectiveness of use of the presented solution, in order to obtain for example the constant depth of the heat affected zone for the geometrically variable hardened axisymmetric objectshttp://www.degruyter.com/view/j/afe.2015.15.issue-1/afe-2015-0022/afe-2015-0022.xml?format=INTHeat treatmentMoving heat sourceArtificial neural networkNumerical modellingSystem of the control of the heating process |
| spellingShingle | Wróbel J. Kulawik A. Bokota A. Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting Archives of Foundry Engineering Heat treatment Moving heat source Artificial neural network Numerical modelling System of the control of the heating process |
| title | Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting |
| title_full | Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting |
| title_fullStr | Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting |
| title_full_unstemmed | Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting |
| title_short | Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting |
| title_sort | artificial neural network to the control of the parameters of the heat treatment process of casting |
| topic | Heat treatment Moving heat source Artificial neural network Numerical modelling System of the control of the heating process |
| url | http://www.degruyter.com/view/j/afe.2015.15.issue-1/afe-2015-0022/afe-2015-0022.xml?format=INT |
| work_keys_str_mv | AT wrobelj artificialneuralnetworktothecontroloftheparametersoftheheattreatmentprocessofcasting AT kulawika artificialneuralnetworktothecontroloftheparametersoftheheattreatmentprocessofcasting AT bokotaa artificialneuralnetworktothecontroloftheparametersoftheheattreatmentprocessofcasting |