A nanotwin-based analytical model to predict dynamics in cryogenic orthogonal machining copper

Cryogenic cooling helps to improve the machining performance and reduce the tool wear. Cryogenic condition could activate these substructures such as deformation twins and dislocation cells. The effects of the substructures are not taken into consideration in the conventional machining models. The conventional models cannot characterize the dynamics in cryogenic machining, i.e., the evolutions of cutting force and temperature with time. Here, considering the effect of the substructures, a new analytical model for metal cutting was proposed to predict the dynamics in cryogenic orthogonal machining. To validate the applicability of the proposed model, the experiments of orthogonal cutting copper at liquid nitrogen temperature and room temperature were conducted. Transmission electron microscope observations show that nanotwins formed in cryogenic cutting copper. The comparisons between experimental cutting forces and the proposed model or the conventional models validate the rationality of the nanotwin-based analytical model. Numerical calculations were further carried out to reveal the underlying mechanism. The periodic oscillation of cutting force in liquid nitrogen condition is a phenomenon of Hopf bifurcation resulting from the formation of nanotwins

Hierarchical-microstructure based modeling for plastic deformation of partial recrystallized copper

Hierarchical microstructure in partial recrystallized materials can simultaneously improve the strength and ductility of metallic materials. Modeling the mechanical behavior of partial recrystallized materials helps to process materials with superior combination of ductility and strength. Here, using experimental characterization, cellular automation (CA) and finite element method, hierarchical-microstructure based modeling was proposed to simulate the tensile deformation of partial recrystallized copper. Firstly, partial recrystallized coppers with different volume fractions of recrystallization were produced by means of extrusion machining and subsequent heat treatment (HT). Uniaxial tensile tests and microstructural observations show that the hierarchical-microstructure of recrystallized grains (RGs) surrounded by elongated subgrains has a significant effect on the mechanical properties. Then, based on the experimental results, a hierarchical-microstructure based plasticity model was developed to describe the yield surface of partial recrystallized materials. CA was further employed to simulate the hierarchical microstructure. By embedding the plasticity model and simulated hierarchical-microstructure in finite element method, a finite element model (FEM) for mechanical behavior of partial recrystallized copper was proposed, where the elongated subgrain with forest dislocation and low angle grain boundary, the RG with few dislocations and twin boundary, and volume fraction of recrystallization were taken into consideration. Finally, the experimental data and the comparison with the conventional plasticity model validate the rationality of the proposed model

Legumain-Specific Near-Infrared Fluorescence “Turn On” for Tumor-Targeted Imaging

Legumain is one of the cysteine proteases which can serve as an essential indicator for cancer diagnosis. Near-infrared (NIR) nanoprobes with fluorescence “Turn On” property are advantageous in cancer diagnosis. However, to the best of our knowledge, using a completely organic NIR nanoprobe to image legumain activity either <i>in vitro</i> or <i>in vivo</i> has not been reported. Herein, employing a CBT-Cys click condensation reaction, we used a rationally designed NIR probe Cys­(StBu)-Ala-Ala-Asn-Lys­(Cy5.5)-CBT (<b>1</b>) to synthesize its nanoprobes <b>1-NPs</b> with self-quenched fluorescence. Cell and animal experiments indicated that our nanoprobes were able to specifically image legumain activity in living cells and tumors with a NIR fluorescence “Turn On” manner. We envision that the nanoprobes could be applied for the diagnosis of legumain-related diseases in the near future

Machine learning assisted design of high entropy alloys with desired property

We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved