Abstract
The flow behavior of the Al–Si–Mg alloy under uniaxial compression loading was investigated at different strain rates (10−3 s−1, 10−2 s−1,10−1 s−1, 100 s−1) at a wide range of temperatures (573 K, 623 K, 673 K, 723 K, 773 K). The results showed that the peak stress increase with the strain rate and decrease with the increase of temperature. According to the measured flow stress curves, a modified Johnson-Cook (J-C) constitutive model taking strain rate effect on thermo softening into account was proposed to delineate the flow behavior. The comparisons between the measured flow curves and the predicted ones showed them to be very close and the average error is 1.65%. The added experiments were also conducted for validating the modified model, and the predicted data well agreed with the measured flow stress curves. That indicated the modified Johnson-Cook model is reliable and can accurately delineate the flow behavior of Al–Si–Mg alloy.
Highlights
Reducing weight of an automobile’s body can reduce pollutant emissions, improve fuel efficiency and make driving safer
The quasi‐static loading responses of Al–Si–Mg alloy are studied by hot compression experiments
It can be observed from the experimental results that the flow stress compression experiments
Summary
Reducing weight of an automobile’s body can reduce pollutant emissions, improve fuel efficiency and make driving safer. The usage of those alloys is still limited as lower mechanical properties are induced by the coarse casting dendritic microstructures and defects. Severe plastic deformation methods, such as friction stir processing [4], equal channel angular pressing [6] and cryorolling [7], are found to effectively eliminate those drawbacks. During those processes, a large amount of plastic deformation is introduced into the material which refines its casting features, thereby improving the mechanical performance of the material [8]. It is significant to establish the optimal process parameters for refining the microstructure and improving the mechanical property. A processing map and the finite element method are the two common means to optimize the process parameters
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