• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.183-185
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• 2008

Forming Limit Diagram(FLD) is a representative tool for evaluating formability of sheet metals. This paper presents a methodology to determine the FLD using Finite Element Method. For predicting the forming limits numerically. Previous methods such as using the thickness strain or the ductile fracture criterion are limited at plane strain domain. These results suggest that behavior of the void growth in sheet metals is different from real one. In contrast to previous methods, a more exact model which takes void growth into account is used. This result agrees with the experimental result qualitatively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.563-564
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1483-1486
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• 2004

Purpose of this paper is to accummulate database of automotive steel sheet from mild steel to high strength steel in cold rolled steel sheets. Physical properties, mainly mechanical properties, of steel sheet are tested and all data are arranged to one sheet. Methods of test are composed of FLD, tensile strength test, chemical composition, surface roughness and product conditions. Finally this database will be helpful to automotive body designers and die designers to design automotive body parts and tools in a material point of view.

• Steel and Composite Structures
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• v.47 no.5
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• pp.601-613
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• 2023

Numerous methods have been proposed in predicting formability of sheet metals based on microstructural and macro-scale properties of sheets. However, there are limited number of papers on the optimization problem to increase formability of sheet metals. In the present study, we aim to use novel optimization algorithms in neural networks to maximize the formability of sheet metals based on tensile curve and texture of aluminum sheet metals. In this regard, experimental and numerical evaluations of effects of texture and tensile properties are conducted. The texture effects evaluation is performed using Taylor homogenization method. The data obtained from these evaluations are gathered and utilized to train and validate an artificial neural network (ANN) with different optimization methods. Several optimization method including grey wolf algorithm (GWA), chimp optimization algorithm (ChOA) and whale optimization algorithm (WOA) are engaged in the optimization problems. The results demonstrated that in aluminum alloys the most preferable texture is cube texture for the most formable sheets. On the other hand, slight differences in the tensile behavior of the aluminum sheets in other similar conditions impose no significant decreases in the forming limit diagram under stretch loading conditions.