445,080 research outputs found

    Computationally efficient representation of statistically described material microstructure for tractable forming simulations

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    The purpose of this project is to reduce a large statistical distribution of metal microstructure orientations to a manageable distribution to be used in metal forming simulations. Microstructure sensitive simulations at the macro-scale are impractical, because with so many state variables associated with material microstructure data, these simulations are extremely computationally expensive. The goal was to develop a framework to accurately model plastic material response while representing the material microstructure in a more compact form, reducing 106 or more microstructure orientations to a significantly smaller statistical distribution of representative orientations. This will significantly increase the computational efficiency and make the design process known as microstructure sensitive design (MSD) feasible for industry applications. This framework is applied to metals with both cubic and hexagonal structure to validate this approach for slip and twinning deformation mechanisms. Performing microstructure sensitive metal-forming simulations is widely recognized as a computational challenge because of the need to store large sets of state variables related to microstructure data. This makes the investigation of the accuracy of smaller, representative data sets in these simulations profitable. The project accomplished two main goals; the development of an effective fitting algorithm to generate compacted data sets and validation of the framework for data compaction on metals with cubic structure, and hexagonal symmetry, with and without twinning. The research was applied to oxygen-free high-conductivity copper (OFHC Cu) and 6016 aluminum (Al-6016) for application of the framework to cubic metals. An anisotropic (clock-rolled) zirconium (Zr) texture was used to develop the framework for hexagonal metals. The minimum accurate data set for cubic was determined to be 825 orientations and for hexagonal metals, considering twinning and absence of twinning, the minimum number was 1600 orientations. This compaction method will increase the computational speed of microstructure sensitive forming simulations by several orders of magnitude, contributing to the computational feasibility of microstructure informed design

    A comprehensive study on the microstructure and mechanical properties of arc girth welded joints of spiral welded high strength API X70 steel pipe

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    In the paper, the effect of welding technology on the microstructure and mechanical properties of girth welded joints was presented. Metallographic examinations based on light microscopy and SEM were conducted on girth welded joints of API X70 steel pipe. Research has shown that microstructure of the heat-affected zone (HAZ) of MMA girth welded joints is not homogeneous and depends on the thermal history of each area during the welding process. Near the fusion line the zone is coarse, and further away there is a fine-grained zone. In the area of root passes the microstructure consists of recrystallized ferrite grains unlike to cap passes where the fine bainitic microstructure can be observed. In the case of MAG girth welded joints, the weld microstructure consists of primary austenite grains. The primary austenite boundaries serve as nucleation sites of ferrite. The microstructure of the HAZ varies continuously from a coarse—to fine-grained microstructure of the base material. The results of mechanical properties of girth welded joints are also presented. The hardness and strength of arc welded joints depend on welding filler materials as well as welding technology. The results of hardness distribution of MMA and MAG girth welded joints confirmed the results of microstructural evaluation

    Computationally efficient representation of statistically described material microstructure for tractable forming simulations

    Get PDF
    The purpose of this project is to reduce a large statistical distribution of metal microstructure orientations to a manageable distribution to be used in metal forming simulations. Microstructure sensitive simulations at the macro-scale are impractical, because with so many state variables associated with material microstructure data, these simulations are extremely computationally expensive. The goal was to develop a framework to accurately model plastic material response while repre- senting the material microstructure in a more compact form, reducing 106 or more microstructure orientations to a significantly smaller statistical distribution of representative orientations. This will significantly increase the computational efficiency and make the design process known as mi- crostructure sensitive design (MSD) feasible for industry applications. This framework is applied to metals with both cubic and hexagonal structure to validate this approach for slip and twinning deformation mechanisms. Performing microstructure sensitive metal-forming simulations is widely recognized as a computational challenge because of the need to store large sets of state variables related to microstructure data. This makes the investigation of the accuracy of smaller, representative data sets in these simulations profitable. The project accomplished two main goals; the development of an effective fitting algorithm to generate compacted data sets and validation of the framework for data compaction on metals with cubic structure, and hexagonal symmetry, with and without twinning. The research was applied to oxygen-free high-conductivity copper (OFHC Cu) and 6016 aluminum (Al-6016) for application of the framework to cubic metals. An anisotropic (clock-rolled) zirconium (Zr) texture was used to develop the framework for hexagonal metals. The minimum accurate data set for cubic was determined to be 825 orientations and for hexagonal metals, considering twinning and absence of twinning, the minimum number was 1600 orientations. This compaction method will increase the computational speed of microstructure sensitive forming simulations by several orders of magnitude, contributing to the computational feasibility of microstructure informed design

    Computational modeling of microstructure

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    Many materials such as martensitic or ferromagnetic crystals are observed to be in metastable states exhibiting a fine-scale, structured spatial oscillation called microstructure; and hysteresis is observed as the temperature, boundary forces, or external magnetic field changes. We have developed a numerical analysis of microstructure and used this theory to construct numerical methods that have been used to compute approximations to the deformation of crystals with microstructure

    Stochastic model for the 3D microstructure of pristine and cyclically aged cathodes in Li-ion batteries

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    It is well-known that the microstructure of electrodes in lithium-ion batteries strongly affects their performance. Vice versa, the microstructure can exhibit strong changes during the usage of the battery due to aging effects. For a better understanding of these effects, mathematical analysis and modeling has turned out to be of great help. In particular, stochastic 3D microstructure models have proven to be a powerful and very flexible tool to generate various kinds of particle-based structures. Recently, such models have been proposed for the microstructure of anodes in lithium-ion energy and power cells. In the present paper, we describe a stochastic modeling approach for the 3D microstructure of cathodes in a lithium-ion energy cell, which differs significantly from the one observed in anodes. The model for the cathode data enhances the ideas of the anode models, which have been developed so far. It is calibrated using 3D tomographic image data from pristine as well as two aged cathodes. A validation based on morphological image characteristics shows that the model is able to realistically describe both, the microstructure of pristine and aged cathodes. Thus, we conclude that the model is suitable to generate virtual, but realistic microstructures of lithium-ion cathodes

    On the correlation structure of microstructure noise in theory and practice

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    We argue for incorporating the financial economics of market microstructure into the financial econometrics of asset return volatility estimation. In particular, we use market microstructure theory to derive the cross-correlation function between latent returns and market microstructure noise, which feature prominently in the recent volatility literature. The cross-correlation at zero displacement is typically negative, and cross-correlations at nonzero displacements are positive and decay geometrically. If market makers are sufficiently risk averse, however, the cross-correlation pattern is inverted. Our results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed cross-correlation patterns, for predicting as-yet undiscovered patterns, and for making informed conjectures as to improved volatility estimation methods
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