A Novel Test Design for Large Strain Uniaxial Reverse Loading of AZ31B Sheet Out of the Rolling Plane

Understanding a magnesium alloy sheet's response to load reversals is important to accurately simulate and optimize a component's manufacturing process. Through this research, the room temperature compression-tension and tension-compression experiments with strains up to ∼12% are performed on AZ31B-H24 sheet specimens along the normal direction of a 6.35 mm-thick sheet. Miniature specimens machined through thickness are tested using a novel setup designed for large strain reverse loading data generation where specimen size is limited. The reliability of the devised setup is verified by finite element simulation and by reproducing in-plane curves obtained via an anti-buckling fixture. A shot peening process involving prevailing through-thickness deformation is modeled and numerical results indicate that employing only in-plane properties of magnesium sheets for simulating such processes can lead to inaccurate predictions.

THE INFLUENCE OF PLASTIC STRAINS ON THE BEHAVIOR OF BENDING REINFORCED CONCRETE ELEMENTS WHEN CHANGING THE FORCE SIGN

The Russian standards for earthquake resistant constructions suppose the development of plastic strains in reinforced concrete constructions under seismic effects. Their presence influences significantly on the stress-strain state, bearing capacity and mechanism of destruction of reinforced concrete constructions under alternation loads of high intensity. The methodology and main results of experimental tests connected with the evaluation of plastic deformations influence on the behavior of reinforced concrete bending elements when changing the force sign is represented. The information about the bearing capacity reduction when changing the force sign with the increase of plastic strains in the first semi cycle of loading is given. The coefficients of plasticity for reinforcement deformations limit values are obtained, corresponding to the beginning of compressed zone concrete destruction when changing the force sign and the plots of normal sections deformations distribution of experimental specimens under direct and reverse loading have been built.

Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach

In this paper, a data-driven approach for constructing elastoplastic constitutive law of microstructured materials is proposed by combining the insights from plasticity theory and the tools of artificial intelligence (i.e., constructing yielding function through ANN) to reduce the required amount of data for machine learning. Illustrative examples show that the constitutive laws constructed by the present approach can be used to solve the boundary value problems (BVPs) involving elastoplastic materials with microstructures under complex loading paths (e.g., cyclic/reverse loading) effectively. The limitation of the proposed approach is also discussed.