2019
DOI: 10.1016/j.ijmecsci.2019.04.020
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Design of the flexibly-reconfigurable roll forming process by a progressively-improving goal seeking approach

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Cited by 14 publications
(4 citation statements)
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“…From Equation (21) to Equation (23), it is found that the expressions of p and q can be written as follows:…”
Section: Modification Of the Formula For Calculating The Longitudinal Curvature Radius Of Saddle Surfacementioning
confidence: 99%
See 1 more Smart Citation
“…From Equation (21) to Equation (23), it is found that the expressions of p and q can be written as follows:…”
Section: Modification Of the Formula For Calculating The Longitudinal Curvature Radius Of Saddle Surfacementioning
confidence: 99%
“…Park et al [20] established a regression model and an artificial neural network model to predict the longitudinal curvature radius, and it was found that the regression model is more suitable for the prediction of convex parts, and the artificial neural network model is more suitable for the prediction of saddle parts. Ghiabakloo et al [21] used the finite element method and genetic algorithm to calculate the longitudinal curvature radius. Cai et al [22][23][24] analyzed the mechanism of the longitudinal bending deformation of the sheet metal from a mechanics perspective, and put forward the necessary condition of CRF; that is, the longitudinal strain is the first-order function of coordinates.…”
Section: Introductionmentioning
confidence: 99%
“…The doubly-curved surface rolling process based on the line forming method, due to its high production e ciency, low equipment cost, and good surface quality of the formed surfaces [5] , has attracted the attention of many scholars in recent years. In 1988, Yamashita and Yamakawa [6] rst proposed the use of roll forming to manufacture doubly-curved surfaces.…”
Section: Introductionmentioning
confidence: 99%
“…Yoon et al [ 21 ] established a prediction model of longitudinal curvature radius by defining deformation vectors. For the purpose of improving the efficiency and accuracy of the prediction, Ghiabakloo et al [ 22 , 23 , 24 ] decomposed the shape prediction into three regions, namely pre-rolling, rolling, and post-rolling, each of which was solved by an appropriate method. Park et al [ 25 , 26 ] constructed a regression model and an artificial neural network model, respectively, and they proved that the regression model is suitable for convex surfaces, and the artificial neural network model performs better while producing saddle surfaces after comparison.…”
Section: Introductionmentioning
confidence: 99%