Increasing elongation and estimating the post-necking strain hardening behavior of AA6016-T4 and AA7021-T79 sheets via the mechanics of cyclic bending under tension
Source abstract
Abstract
This paper reports a substantially improved elongation-to-failure (ETF) and infers the post-necking strain hardening behavior of aluminum alloy (AA)6016-T4 and AA7021-T79 sheets by subjecting the sheets to a cyclic bending under tension (CBT) process. The process parameters bend depth and pull speed were adjusted via roller depth and crosshead velocity to maximize the ETF of the alloys in the rolling and transverse directions. Remarkably, about five times improved ETF of AA6016-T4 was achieved using the optimized parameters of 1.5 mm/s crosshead velocity and 4 mm bending depth, while three times improved ETF of AA7021-T79 was achieved using 1 mm/s crosshead velocity and 2.5 mm bending depth relative to corresponding simple tension (ST) tests. The smaller improvements of the less ductile AA7021-T79 were attributed to the smaller non-uniform deformation capacity of the alloy after exhausting the uniform elongation in tension resulting in a smaller amount of the remaining ductility to deplete throughout the sheet by CBT. The strength levels not attainable via ST testing and remaining ductility were measured for the alloys by creating sub-size specimens from the interrupted CBT test and testing in ST. The yield stresses from these ST tests were shifted by the axial strains accumulated during the pre-CBT deformation to delineate flow curves to large strains. The estimated large strain flow curves were used to simulate load-displacement curves per alloy in Abaqus for comparisons with the recorded data during CBT. The utility of CBT testing and modeling to infer the post-necking strain hardening behavior of sheet metals was demonstrated.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aluminum Alloy Microstructure Properties · Metal Forming Simulation Techniques · Metallurgy and Material Forming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (92)
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