Material parameters in void growth model for G20Mn5QT cast steel at low temperatures
Source abstract
Abstract
Abstract With superior structural performances, G20Mn5QT cast steel has been extensively used in fabricating complex joints in steel constructions. Fracture failure of G20Mn5QT cast steel is then an important issue, especially for low-temperature situations. In this paper, mechanical properties of G20Mn5QT cast steel at low temperatures were studied by tensile coupon tests and Charpy impact tests. The effects of the low temperature on mechanical properties and the ductile-brittle transition temperature of G20Mn5QT cast steel were obtained based on the test results. The void growth model (VGM) and the stress modified critical strain (SMCS) model, which are two typical micromechanical fracture models, were calibrated for G20Mn5QT cast steel by experiments and complementary FEA on smooth notched tensile (SNT) specimens at four temperature levels, 20°C, −20°C, −40°C and −60°C. For each temperature level, the material parameters in VGM and SMCS calculated based on test results of all six different specimens are in very good consistence. This consistency indicates the applicability of VGM and SMCS for predicting the ductile fracture of G20Mn5QT cast steel, even at the temperature of −60°C, which is much lower than the transition temperature of the material. The calibrated material parameters representing the resistance to ductile fracture of the material decrease linearly with the drop of the temperature. The characteristic length of G20Mn5QT cast steel was also determined by observation of fractured surfaces of test specimens with the scanning electron microscope, which has the tendency to decrease with the drop of the temperature.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metal Forming Simulation Techniques · Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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