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Predicting springback radii and angles in air bending of high-strength sheet steel through gaussian process regressions
https://orcid.org/http://orcid.org/0000-0002-9464-1751
Machine learning approaches can help facilitate the optimization of machining processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In this study, we develop the Gaussian process regression (GPR) models to predict springback radii and angles for air bending of high-strength sheet steel from the sheet geometry, tool design, and metal properties. The models are simple and accurate and stable for the data considered, which might contribute to fast springback radius and angle estimations. By combining the optimization results from the Taguchi method and GPR approach, it might be expected that more quantitative data can be extracted from fewer experimental trials at the same time.
Predicting springback radii and angles in air bending of high-strength sheet steel through gaussian process regressions
https://orcid.org/http://orcid.org/0000-0002-9464-1751
Machine learning approaches can help facilitate the optimization of machining processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In this study, we develop the Gaussian process regression (GPR) models to predict springback radii and angles for air bending of high-strength sheet steel from the sheet geometry, tool design, and metal properties. The models are simple and accurate and stable for the data considered, which might contribute to fast springback radius and angle estimations. By combining the optimization results from the Taguchi method and GPR approach, it might be expected that more quantitative data can be extracted from fewer experimental trials at the same time.
Predicting springback radii and angles in air bending of high-strength sheet steel through gaussian process regressions
https://orcid.org/http://orcid.org/0000-0002-9464-1751
Machine learning approaches can help facilitate the optimization of machining processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In this study, we develop the Gaussian process regression (GPR) models to predict springback radii and angles for air bending of high-strength sheet steel from the sheet geometry, tool design, and metal properties. The models are simple and accurate and stable for the data considered, which might contribute to fast springback radius and angle estimations. By combining the optimization results from the Taguchi method and GPR approach, it might be expected that more quantitative data can be extracted from fewer experimental trials at the same time.
Predicting springback radii and angles in air bending of high-strength sheet steel through gaussian process regressions
Int J Interact Des Manuf
Zhang, Yun (Autor:in) / Xu, Xiaojie (Autor:in)
01.09.2022
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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