The effect of cold sprayed coatings on the mechanical properties of AZ91D magnesium alloys

Although many previous studies have confirmed that cold sprayed Al+xAlO (x=0-75vol.%) composite coatings on magnesium alloys can effectively improve wear and corrosion resistance, the effect of such coatings on tensile and fatigue properties is still unclear. The present work aims to evaluate the tensile properties and fatigue resistance of an AZ91D alloy with cold sprayed Al+xAlO (x=0, 30, 50vol.%) coatings. The experimental results showed that although the cold spray coating leads to an increase in yield strength and fatigue limit, the tensile strength is reduced. In-situ examination of the macro-morphology of surfaces of the coated specimens during tensile testing was carried out through video recording. It indicated that once the tensile specimens have yielded, horizontal cracks that were perpendicular to the tensile axis formed on the surface of the specimens. Fractographic analysis of the fracture surfaces of the tensile specimens in a scanning electron microscope revealed that all the cracks within the cold sprayed coatings were suspended at the coating/substrate interface. Based on these experimental observations, it is considered that the improved yield strength is attributed to the constraint effect of the cold sprayed composite coatings on the magnesium substrate. The enhanced fatigue limit is a result of the higher yield strength and the coating/substrate interface barrier to crack propagation. The decrease in tensile strength is attributed to the brittle fracture of the cold sprayed coatings when yielding of the specimens

Characterization of four-qubit states via Bell inequalities

A set of Bell inequalities classifying the quantum entanglement of four-qubit states is presented. These inequalities involve only two measurement settings per observer and can characterize fully separable, bi-separable and tri-separable quantum states. In addition, a quadratic inequality of the Bell operators for four-qubit systems is derived

Co-Salient Object Detection with Co-Representation Purification

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irrelevant information in the co-representation interferes with its locating of co-salient objects. In this paper, we propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation. We search a few pixel-wise embeddings probably belonging to co-salient regions. These embeddings constitute our co-representation and guide our prediction. For obtaining purer co-representation, we use the prediction to iteratively reduce irrelevant embeddings in our co-representation. Experiments on three datasets demonstrate that our CoRP achieves state-of-the-art performances on the benchmark datasets. Our source code is available at https://github.com/ZZY816/CoRP.Comment: Accepted by TPAMI 202