STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION
Surface hardness is an important index for evaluating the quality of surface processing. Ultrasonic rolling and extrusion technology plays a very significant role in surface strengthening. Taking 42 CrMo bearing steel as the object,the range analysis was carried out on the orthogonal experiment resu...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2020-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.008 |
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| _version_ | 1841535807068831744 |
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| author | WANG XiaoQiang RUAN XiaoLin CUI FengKui LIU Fei |
| author_facet | WANG XiaoQiang RUAN XiaoLin CUI FengKui LIU Fei |
| author_sort | WANG XiaoQiang |
| collection | DOAJ |
| description | Surface hardness is an important index for evaluating the quality of surface processing. Ultrasonic rolling and extrusion technology plays a very significant role in surface strengthening. Taking 42 CrMo bearing steel as the object,the range analysis was carried out on the orthogonal experiment results of ultrasonic rolling extrusion,and the significance of process parameters on surface hardness was obtained. The reliability and accuracy of BP neural network model and stepwise regression model were compared and analyzed in sections by using k-fold cross validation method. This process fully considers the fitting ability and prediction ability of the model. The results show that the validation error range and average error of the stepwise regression model are smaller,and the prediction accuracy of the model is higher. Finally,the established prediction model of surface hardness has strong overall and coefficient significance,which can be applied to the optimization and improvement of surface quality in ultrasonic rolling extrusion. |
| format | Article |
| id | doaj-art-bc4e43771f43441a94b6213d8949089e |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-bc4e43771f43441a94b6213d8949089e2025-01-15T02:27:25ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-014281181630608309STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSIONWANG XiaoQiangRUAN XiaoLinCUI FengKuiLIU FeiSurface hardness is an important index for evaluating the quality of surface processing. Ultrasonic rolling and extrusion technology plays a very significant role in surface strengthening. Taking 42 CrMo bearing steel as the object,the range analysis was carried out on the orthogonal experiment results of ultrasonic rolling extrusion,and the significance of process parameters on surface hardness was obtained. The reliability and accuracy of BP neural network model and stepwise regression model were compared and analyzed in sections by using k-fold cross validation method. This process fully considers the fitting ability and prediction ability of the model. The results show that the validation error range and average error of the stepwise regression model are smaller,and the prediction accuracy of the model is higher. Finally,the established prediction model of surface hardness has strong overall and coefficient significance,which can be applied to the optimization and improvement of surface quality in ultrasonic rolling extrusion.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.008Ultrasonic rolling extrusionBP neural networkStepwise regressionPrediction modelSurface Hardness |
| spellingShingle | WANG XiaoQiang RUAN XiaoLin CUI FengKui LIU Fei STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION Jixie qiangdu Ultrasonic rolling extrusion BP neural network Stepwise regression Prediction model Surface Hardness |
| title | STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION |
| title_full | STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION |
| title_fullStr | STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION |
| title_full_unstemmed | STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION |
| title_short | STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION |
| title_sort | study on prediction model of surface hardness in ultrasound rolling extrusion |
| topic | Ultrasonic rolling extrusion BP neural network Stepwise regression Prediction model Surface Hardness |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.008 |
| work_keys_str_mv | AT wangxiaoqiang studyonpredictionmodelofsurfacehardnessinultrasoundrollingextrusion AT ruanxiaolin studyonpredictionmodelofsurfacehardnessinultrasoundrollingextrusion AT cuifengkui studyonpredictionmodelofsurfacehardnessinultrasoundrollingextrusion AT liufei studyonpredictionmodelofsurfacehardnessinultrasoundrollingextrusion |