Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

Springback in multi-point dieless forming (MDF) is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time...

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Main Authors: Misganaw Abebe, Jun-Seok Yoon, Beom-Soo Kang
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Metals
Subjects:
Online Access:https://www.mdpi.com/2075-4701/7/12/528
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spelling doaj-a724d4db74e54ea6a9d5cca11a0643b22020-11-25T00:38:55ZengMDPI AGMetals2075-47012017-11-0171252810.3390/met7120528met7120528Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and CompensationMisganaw Abebe0Jun-Seok Yoon1Beom-Soo Kang2Department of Aerospace Engineering, Pusan National University, Busan 46241, KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, KoreaSpringback in multi-point dieless forming (MDF) is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF) to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE). Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE) simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.https://www.mdpi.com/2075-4701/7/12/528multi-point dieless formingspringback reductionspringback compensationradial basis function
collection DOAJ
language English
format Article
sources DOAJ
author Misganaw Abebe
Jun-Seok Yoon
Beom-Soo Kang
spellingShingle Misganaw Abebe
Jun-Seok Yoon
Beom-Soo Kang
Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
Metals
multi-point dieless forming
springback reduction
springback compensation
radial basis function
author_facet Misganaw Abebe
Jun-Seok Yoon
Beom-Soo Kang
author_sort Misganaw Abebe
title Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
title_short Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
title_full Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
title_fullStr Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
title_full_unstemmed Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
title_sort radial basis functional model of multi-point dieless forming process for springback reduction and compensation
publisher MDPI AG
series Metals
issn 2075-4701
publishDate 2017-11-01
description Springback in multi-point dieless forming (MDF) is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF) to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE). Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE) simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.
topic multi-point dieless forming
springback reduction
springback compensation
radial basis function
url https://www.mdpi.com/2075-4701/7/12/528
work_keys_str_mv AT misganawabebe radialbasisfunctionalmodelofmultipointdielessformingprocessforspringbackreductionandcompensation
AT junseokyoon radialbasisfunctionalmodelofmultipointdielessformingprocessforspringbackreductionandcompensation
AT beomsookang radialbasisfunctionalmodelofmultipointdielessformingprocessforspringbackreductionandcompensation
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