An innovative tester system for measuring mechanical property of foundry molding sand

A new intelligent tester system for measuring multiple mechanical properties of foundry molding sand is introduced and has been patented for the invention in China. The testing process can be simutaneosly controlled wth a build-in chip microcomputer communicating with a PC through a serial port. The testing system pplies dynamic testing technology. During the measurement for compression, relaxation, shearing and tensile processes of sand specimens, the corresponding characteristic curves and eight mechanical property parameters can be obtained in a short time, simply by consecutively testing on four sand specimens. The properties and parameters to be measurable by the tester include compressive strength, elastic modulus, plastic deformation threshold, springback potential, shear strength, shear deformation limit, toughness and tensile strength. These properties and parameters for sand specimens can be defined as the corresponding characteristic curves with precise physical meanings, carried out by the tester. Two of them, namely plastic deformation threshold and springback potential, as well as their testing methods, have been invented for the first time. The testing system applying advanced data measurement technology as well as performing excellent functions is an important breakthrough and creativity in foundry molding sand property testing field. The parameters acquired by the testing system are stable, accurate and reliable. The test data can be instantly diaplayed or printed out or stored in the PC. As evidence, many experimental data obtained by the tester practically from bth laboratory and foundry floor tests indicate that the testr system can be widely applied in foundry industry

Measurement of elastic modulus and evaluation of viscoelasticity of foundry green sand

Elastic modulus is an important physical parameter of molding sand; it is closely connected with molding sand's properties. Based on theories of rheology and molding sand microdeformation, elastic modulus of molding sand was measured and investigated using the intelligent molding sand multi-property tester developed by ourselves. The measuring principle was introduced. Effects of bentonite percentage and compactibility of the molding sand were experimentally studied. Furthermore, the essential viscoelastic nature of green sand was analyzed. It is considered that viscoelastic deformation of molding sand consists mainly of that of Kelvin Body of clay membrane, and elastic modulus of molding sand depends mainly on that of Kelvin Body which is the elastic component of clay membrane between sands. Elastic modulus can be adopted as one of the property parameters, and can be employed to evaluate viscoelastic properties of molding sand

A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images

Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity