Browse Publications Technical Papers 2018-01-0109
2018-04-03

Fracture Characteristic Prediction of High-Strength Aluminum Alloy Extrusion using Cockcroft-Latham Ductile Fracture Criteria 2018-01-0109

Demands are increasing for the reduction of vehicle weight to enhance automobile fuel efficiency and driving performance, with the use of aluminum alloys expected to help. High-strength aluminum alloys (6xxx series, 7xxx series) are called for to enhance crash safety performance, and the prediction of material fracture is a key factor in the application of these alloys.
This research presents a FEM model that can predict both tensile fracture and bending fracture when large deformations occur in the extrusion direction of high-strength aluminum alloy extrusion.
The fracture characteristics of high-strength aluminum alloy extrusion were obtained by tensile and bending tests, and the factors governing ductile performance were clarified. Fracture was defined in the FEM model using the Cockcroft-Latham ductile fracture model. In addition, the surface crystal grain of aluminum extrusion becomes coarse as a result of the extrusion process, and the hardness distribution also exhibits a soft surface layer. Therefore, a definition that varies the material properties in the plate thickness direction, using the definition of laminate material as the composite material, was added to the FEM modeling process.
FEM structural analysis was performed with tensile and bending tests using these definitions, and the analysis accuracy was verified. The results showed that the FEM structural analysis of tensile and bending tests reproduced the experimental results for load and stroke fractures to within an error of 10%.
In order to describe tensile fracture and bending fracture in the direction of high-strength aluminum alloy extrusion using FEM, the Cockcroft-Latham ductile fracture model was combined with a method of varying the material properties in the plate thickness direction, according to the definition of laminate material. This enabled an accurate fracture load and stroke prediction within an error of 10%.

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