Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression
Single point incremental forming （SPIF） is a highly flexible manufacturing process widely utilized in the aerospace industry， particularly suited for customized and small-batch production components. However， the appropriate range of process parameters suitable for different models remains undefined...
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Journal of Materials Engineering
2025-04-01
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| Series: | Cailiao gongcheng |
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| Online Access: | https://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2024.000847 |
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| author | LIANG Zhikai ZHANG Zhichao HU Lan PANG Qiu |
| author_facet | LIANG Zhikai ZHANG Zhichao HU Lan PANG Qiu |
| author_sort | LIANG Zhikai |
| collection | DOAJ |
| description | Single point incremental forming （SPIF） is a highly flexible manufacturing process widely utilized in the aerospace industry， particularly suited for customized and small-batch production components. However， the appropriate range of process parameters suitable for different models remains undefined， necessitating extensive parameter testing. An orthogonal experiment is conducted to perform a multi-factor analysis of variance， discussing the influence of parameters such as sheet thickness， angle， incremental amount， feed rate， and rotational speed on the maximum forming depth. Based on the experimental results， a regression model using the Adaboost algorithm is developed to predict the forming depth of 6061 aluminum alloy thin sheets at the forming diameter of 100 mm. The results indicate that the influences of single factors on the maximum forming depth in descending order of significance are： thickness， layer increment， angle， feed rate， and rotational speed. Under the optimal forming conditions achieved at the fastest forming speed， the maximum forming angle is 70°， the sheet thickness is 1 mm， the layer increment is 0.2 mm， the feed rate is 2000 mm/min， and the rotational speed is 2000 r/min. Furthermore， the regression model created based on the orthogonal experiment demonstrates high accuracy， correlating well with both the Abaqus simulation results and the actual experimental outcomes. The maximum error between the four groups of tests and simulations is 4.24%， while the maximum error with the actual forming results is -2.45%. |
| format | Article |
| id | doaj-art-6e2efcd1549c496d8b5d468d131ddcbb |
| institution | OA Journals |
| issn | 1001-4381 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Journal of Materials Engineering |
| record_format | Article |
| series | Cailiao gongcheng |
| spelling | doaj-art-6e2efcd1549c496d8b5d468d131ddcbb2025-08-20T02:29:55ZzhoJournal of Materials EngineeringCailiao gongcheng1001-43812025-04-01534233410.11868/j.issn.1001-4381.2024.0008471001-4381（2025）04-0023-12Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regressionLIANG Zhikai0ZHANG Zhichao1HU Lan2PANG Qiu3Foshan Xianhu Laboratory，Foshan 528200，Guangdong，ChinaShanghai Aerospace Equipments Manufacturer Co.， Ltd.，Shanghai 200245，ChinaShanghai Aerospace Equipments Manufacturer Co.， Ltd.，Shanghai 200245，ChinaSchool of Mechanical and Electrical Engineering，Wuhan Donghu University， Wuhan 430212，ChinaSingle point incremental forming （SPIF） is a highly flexible manufacturing process widely utilized in the aerospace industry， particularly suited for customized and small-batch production components. However， the appropriate range of process parameters suitable for different models remains undefined， necessitating extensive parameter testing. An orthogonal experiment is conducted to perform a multi-factor analysis of variance， discussing the influence of parameters such as sheet thickness， angle， incremental amount， feed rate， and rotational speed on the maximum forming depth. Based on the experimental results， a regression model using the Adaboost algorithm is developed to predict the forming depth of 6061 aluminum alloy thin sheets at the forming diameter of 100 mm. The results indicate that the influences of single factors on the maximum forming depth in descending order of significance are： thickness， layer increment， angle， feed rate， and rotational speed. Under the optimal forming conditions achieved at the fastest forming speed， the maximum forming angle is 70°， the sheet thickness is 1 mm， the layer increment is 0.2 mm， the feed rate is 2000 mm/min， and the rotational speed is 2000 r/min. Furthermore， the regression model created based on the orthogonal experiment demonstrates high accuracy， correlating well with both the Abaqus simulation results and the actual experimental outcomes. The maximum error between the four groups of tests and simulations is 4.24%， while the maximum error with the actual forming results is -2.45%.https://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2024.000847spifprocess parameter6061 aluminum alloyadaboost algorithmregression model |
| spellingShingle | LIANG Zhikai ZHANG Zhichao HU Lan PANG Qiu Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression Cailiao gongcheng spif process parameter 6061 aluminum alloy adaboost algorithm regression model |
| title | Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression |
| title_full | Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression |
| title_fullStr | Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression |
| title_full_unstemmed | Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression |
| title_short | Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression |
| title_sort | prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on adaboost regression |
| topic | spif process parameter 6061 aluminum alloy adaboost algorithm regression model |
| url | https://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2024.000847 |
| work_keys_str_mv | AT liangzhikai predictionofmaximumformingdepthinsinglepointincrementalformingof6061aluminumalloybasedonadaboostregression AT zhangzhichao predictionofmaximumformingdepthinsinglepointincrementalformingof6061aluminumalloybasedonadaboostregression AT hulan predictionofmaximumformingdepthinsinglepointincrementalformingof6061aluminumalloybasedonadaboostregression AT pangqiu predictionofmaximumformingdepthinsinglepointincrementalformingof6061aluminumalloybasedonadaboostregression |