Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters
This paper describes the development of a generative-adversarial neural network for generating metal alloy compounds with given parameters. The resulting alloy is described by 19 parameters: 14 describe the alloy composition and 5 describe the alloy properties. At the stage of data preparation the p...
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/06/epjconf_apitech-vii2025_02009.pdf |
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| author | Zhuro Dmitrii Viatkin Dmitry Tsykarev Andrey |
| author_facet | Zhuro Dmitrii Viatkin Dmitry Tsykarev Andrey |
| author_sort | Zhuro Dmitrii |
| collection | DOAJ |
| description | This paper describes the development of a generative-adversarial neural network for generating metal alloy compounds with given parameters. The resulting alloy is described by 19 parameters: 14 describe the alloy composition and 5 describe the alloy properties. At the stage of data preparation the parameters are normalized to the range from 0 to 1. The generator in the generative-adversarial network has 4 input layers. The first input layer receives noise to generate different realistic parameters for the same input values. The second input layer is a mask describing the known and unknown parameters. To the third input layer, the minimum acceptable parameter values are passed. To the fourth input layer of the generator the maximum allowable values of parameters are transferred. Based on the input parameters, at the output of the generator we get 19 parameters describing the alloy. The result of the generator is checked by the discriminator for the reliability of the prediction. The discriminator has 4 input layers. The first one receives the prediction made by the generator. The other 3 inputs receive data from the 2nd, 3rd and 4th input layers of the generator. The generative-adversarial neural network is capable of generating the composition and properties of alloys with an average absolute error of 0.082 units relative to the normalized range of test data parameters, i.e. with an accuracy of 91.8% relative to the real value. |
| format | Article |
| id | doaj-art-4dcaef4e2a71451991224c64d9b7416b |
| institution | DOAJ |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-4dcaef4e2a71451991224c64d9b7416b2025-08-20T03:07:01ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013210200910.1051/epjconf/202532102009epjconf_apitech-vii2025_02009Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parametersZhuro Dmitrii0Viatkin Dmitry1Tsykarev Andrey2Northern Arctic Federal UniversityNorthern Arctic Federal UniversityNorthern Arctic Federal UniversityThis paper describes the development of a generative-adversarial neural network for generating metal alloy compounds with given parameters. The resulting alloy is described by 19 parameters: 14 describe the alloy composition and 5 describe the alloy properties. At the stage of data preparation the parameters are normalized to the range from 0 to 1. The generator in the generative-adversarial network has 4 input layers. The first input layer receives noise to generate different realistic parameters for the same input values. The second input layer is a mask describing the known and unknown parameters. To the third input layer, the minimum acceptable parameter values are passed. To the fourth input layer of the generator the maximum allowable values of parameters are transferred. Based on the input parameters, at the output of the generator we get 19 parameters describing the alloy. The result of the generator is checked by the discriminator for the reliability of the prediction. The discriminator has 4 input layers. The first one receives the prediction made by the generator. The other 3 inputs receive data from the 2nd, 3rd and 4th input layers of the generator. The generative-adversarial neural network is capable of generating the composition and properties of alloys with an average absolute error of 0.082 units relative to the normalized range of test data parameters, i.e. with an accuracy of 91.8% relative to the real value.https://www.epj-conferences.org/articles/epjconf/pdf/2025/06/epjconf_apitech-vii2025_02009.pdf |
| spellingShingle | Zhuro Dmitrii Viatkin Dmitry Tsykarev Andrey Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters EPJ Web of Conferences |
| title | Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| title_full | Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| title_fullStr | Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| title_full_unstemmed | Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| title_short | Neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| title_sort | neural network for generating composition and parameters of metal alloys based on a given range of known and unknown parameters |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/06/epjconf_apitech-vii2025_02009.pdf |
| work_keys_str_mv | AT zhurodmitrii neuralnetworkforgeneratingcompositionandparametersofmetalalloysbasedonagivenrangeofknownandunknownparameters AT viatkindmitry neuralnetworkforgeneratingcompositionandparametersofmetalalloysbasedonagivenrangeofknownandunknownparameters AT tsykarevandrey neuralnetworkforgeneratingcompositionandparametersofmetalalloysbasedonagivenrangeofknownandunknownparameters |