A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature
The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous cooli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/17/1/97 |
| _version_ | 1797358514094473216 |
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| author | Jacek Trzaska Wojciech Sitek |
| author_facet | Jacek Trzaska Wojciech Sitek |
| author_sort | Jacek Trzaska |
| collection | DOAJ |
| description | The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs. |
| first_indexed | 2024-03-08T15:03:06Z |
| format | Article |
| id | doaj.art-21e606f024c1427dbf4de94ad74807f7 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-08T15:03:06Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-21e606f024c1427dbf4de94ad74807f72024-01-10T15:02:35ZengMDPI AGMaterials1996-19442023-12-011719710.3390/ma17010097A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing TemperatureJacek Trzaska0Wojciech Sitek1Department of Engineering Materials and Biomaterials, Faculty of Mechanical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandScientific and Didactic Laboratory of Nanotechnology and Material Technologies, Faculty of Mechanical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandThe article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs.https://www.mdpi.com/1996-1944/17/1/97steelartificial neural networksgenetic algorithmoptimizationmaterials by designheat treatment |
| spellingShingle | Jacek Trzaska Wojciech Sitek A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature Materials steel artificial neural networks genetic algorithm optimization materials by design heat treatment |
| title | A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature |
| title_full | A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature |
| title_fullStr | A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature |
| title_full_unstemmed | A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature |
| title_short | A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature |
| title_sort | hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature |
| topic | steel artificial neural networks genetic algorithm optimization materials by design heat treatment |
| url | https://www.mdpi.com/1996-1944/17/1/97 |
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