Heat treatment control technology of high-strength steel gears based on support vector machine
Abstract In the actual production process of gears often because of the selection of heat treatment parameters is unreasonable and can not accurately achieve the small deformation, high precision, less grinding machining allowance heat treatment sample requirements, there are uneven distribution of...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92312-1 |
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| author | Yanzhong Wang Libin Zhang Yulu Su Hai Liu HaiLong Yang Yanyan Chen |
| author_facet | Yanzhong Wang Libin Zhang Yulu Su Hai Liu HaiLong Yang Yanyan Chen |
| author_sort | Yanzhong Wang |
| collection | DOAJ |
| description | Abstract In the actual production process of gears often because of the selection of heat treatment parameters is unreasonable and can not accurately achieve the small deformation, high precision, less grinding machining allowance heat treatment sample requirements, there are uneven distribution of carburized layer, surface hardness, hardness of the heart can not meet the requirements of the indicators. At the present stage, the method of multi-parameter multi-level combination test block trial production is often used, but its production cycle is long, and the waste of human and material resources is serious. In this study, with the help of machine learning, a support vector machine prediction model of gear tissue distribution is constructed based on heat treatment parameters, and the radial basis functions kernel function is selected as the kernel function of the support vector machine to improve the accuracy of model prediction by optimizing the kernel parameters. The root mean square error value of the final model is 3.16%, and the coefficient of determination is 0.993. The results show that the method of this paper can accurately and efficiently predict the heat treatment results of gears, and save the manufacturing cycle and cost. The precise control of hardness, carburization layer distribution pattern and metallographic organization of ultra-high-strength steel gears can be realized in actual production. |
| format | Article |
| id | doaj-art-4b7210e18efe4abeb7df28e6ca9654bf |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4b7210e18efe4abeb7df28e6ca9654bf2025-08-20T02:59:28ZengNature PortfolioScientific Reports2045-23222025-03-0115111910.1038/s41598-025-92312-1Heat treatment control technology of high-strength steel gears based on support vector machineYanzhong Wang0Libin Zhang1Yulu Su2Hai Liu3HaiLong Yang4Yanyan Chen5School of Mechanical Engineering and Automation, Beihang UniversitySchool of Mechanical Engineering and Automation, Beihang UniversitySchool of Mechanical Engineering and Automation, Beihang UniversityTechnology Center Process Room 2, Chongqing Tiema Transmission Co., LtdTechnology Center Process Room 2, Chongqing Tiema Transmission Co., LtdAVIC Beijing Changcheng Aeronautical Measurement and Control Technology Research InstituteAbstract In the actual production process of gears often because of the selection of heat treatment parameters is unreasonable and can not accurately achieve the small deformation, high precision, less grinding machining allowance heat treatment sample requirements, there are uneven distribution of carburized layer, surface hardness, hardness of the heart can not meet the requirements of the indicators. At the present stage, the method of multi-parameter multi-level combination test block trial production is often used, but its production cycle is long, and the waste of human and material resources is serious. In this study, with the help of machine learning, a support vector machine prediction model of gear tissue distribution is constructed based on heat treatment parameters, and the radial basis functions kernel function is selected as the kernel function of the support vector machine to improve the accuracy of model prediction by optimizing the kernel parameters. The root mean square error value of the final model is 3.16%, and the coefficient of determination is 0.993. The results show that the method of this paper can accurately and efficiently predict the heat treatment results of gears, and save the manufacturing cycle and cost. The precise control of hardness, carburization layer distribution pattern and metallographic organization of ultra-high-strength steel gears can be realized in actual production.https://doi.org/10.1038/s41598-025-92312-1Support vector machineHigh-strength steel gearsCarburizing-quenchingMachine learningPrecise control |
| spellingShingle | Yanzhong Wang Libin Zhang Yulu Su Hai Liu HaiLong Yang Yanyan Chen Heat treatment control technology of high-strength steel gears based on support vector machine Scientific Reports Support vector machine High-strength steel gears Carburizing-quenching Machine learning Precise control |
| title | Heat treatment control technology of high-strength steel gears based on support vector machine |
| title_full | Heat treatment control technology of high-strength steel gears based on support vector machine |
| title_fullStr | Heat treatment control technology of high-strength steel gears based on support vector machine |
| title_full_unstemmed | Heat treatment control technology of high-strength steel gears based on support vector machine |
| title_short | Heat treatment control technology of high-strength steel gears based on support vector machine |
| title_sort | heat treatment control technology of high strength steel gears based on support vector machine |
| topic | Support vector machine High-strength steel gears Carburizing-quenching Machine learning Precise control |
| url | https://doi.org/10.1038/s41598-025-92312-1 |
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