Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion
Abstract In response to the difficulty in predicting the change of bolt preload when using torque method to load bolt, this paper proposes a bolt preload prediction method based on mechanism and data fusion calculation for hexagonal end face bolt, and establishes a tightening prediction model based...
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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/s41598-025-88213-y |
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| author | Yueqi Qiao Bing Zhao Dingshan Deng Weijin Ouyang |
| author_facet | Yueqi Qiao Bing Zhao Dingshan Deng Weijin Ouyang |
| author_sort | Yueqi Qiao |
| collection | DOAJ |
| description | Abstract In response to the difficulty in predicting the change of bolt preload when using torque method to load bolt, this paper proposes a bolt preload prediction method based on mechanism and data fusion calculation for hexagonal end face bolt, and establishes a tightening prediction model based on machine learning method. Firstly, a tightening mechanism model is established, revealing the reasons why bolt preload is difficult to predict and errors cannot be eliminated. Secondly, sensitivity evaluation indicator is established to conduct parameter sensitivity analysis, and the fusion method of “mechanism model guiding data model to perform feature selection” is determined. Finally, the tightening prediction model based on Gaussian Process Regression is proposed, and corresponding engineering prediction software is established. The experimental results show that this prediction model can not only predict the variation of bolt preload with tightening torque, but also synchronously display the confidence interval of bolt preload fluctuation in a probabilistic sense. Under different operating conditions, the prediction accuracy still remains above 98.18%. The prediction model breaks through the limitation of traditional method, which calculates the torque coefficient and indirectly loads the bolt preload. |
| format | Article |
| id | doaj-art-e9cd8a49a3b74b43a15c4adf5d2427bd |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e9cd8a49a3b74b43a15c4adf5d2427bd2025-08-20T01:57:51ZengNature PortfolioScientific Reports2045-23222025-03-0115111510.1038/s41598-025-88213-yPrediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusionYueqi Qiao0Bing Zhao1Dingshan Deng2Weijin Ouyang3Department of Mechanical Engineering, Qinghai UniversityDepartment of Mechanical Engineering, Qinghai UniversityDepartment of Mechanical Engineering, Qinghai UniversityDepartment of Mechanical Engineering, Qinghai UniversityAbstract In response to the difficulty in predicting the change of bolt preload when using torque method to load bolt, this paper proposes a bolt preload prediction method based on mechanism and data fusion calculation for hexagonal end face bolt, and establishes a tightening prediction model based on machine learning method. Firstly, a tightening mechanism model is established, revealing the reasons why bolt preload is difficult to predict and errors cannot be eliminated. Secondly, sensitivity evaluation indicator is established to conduct parameter sensitivity analysis, and the fusion method of “mechanism model guiding data model to perform feature selection” is determined. Finally, the tightening prediction model based on Gaussian Process Regression is proposed, and corresponding engineering prediction software is established. The experimental results show that this prediction model can not only predict the variation of bolt preload with tightening torque, but also synchronously display the confidence interval of bolt preload fluctuation in a probabilistic sense. Under different operating conditions, the prediction accuracy still remains above 98.18%. The prediction model breaks through the limitation of traditional method, which calculates the torque coefficient and indirectly loads the bolt preload.https://doi.org/10.1038/s41598-025-88213-yBolted connectionMechanism modelMechanism and data fusionGaussian process regressionPrediction model |
| spellingShingle | Yueqi Qiao Bing Zhao Dingshan Deng Weijin Ouyang Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion Scientific Reports Bolted connection Mechanism model Mechanism and data fusion Gaussian process regression Prediction model |
| title | Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| title_full | Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| title_fullStr | Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| title_full_unstemmed | Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| title_short | Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| title_sort | prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion |
| topic | Bolted connection Mechanism model Mechanism and data fusion Gaussian process regression Prediction model |
| url | https://doi.org/10.1038/s41598-025-88213-y |
| work_keys_str_mv | AT yueqiqiao predictionmodelfornonlinearvariationofboltpreloadwithtighteningtorquebasedonmechanismanddatafusion AT bingzhao predictionmodelfornonlinearvariationofboltpreloadwithtighteningtorquebasedonmechanismanddatafusion AT dingshandeng predictionmodelfornonlinearvariationofboltpreloadwithtighteningtorquebasedonmechanismanddatafusion AT weijinouyang predictionmodelfornonlinearvariationofboltpreloadwithtighteningtorquebasedonmechanismanddatafusion |