In order to overcome the trade-offbetween strength and ductility in traditional metallic materials,the gradient lamellar structure was fabricated through an ultrasound-aided deep rolling technique in pure Ni with high...
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In order to overcome the trade-offbetween strength and ductility in traditional metallic materials,the gradient lamellar structure was fabricated through an ultrasound-aided deep rolling technique in pure Ni with high stacking fault energy after heat *** gradient lamellar Ni was successively di-vided into three ***-situ micro-tensile tests were performed in different regions to reveal the corresponding microscopic mechanical *** characterization techniques were adopted to explore the effects of microstructural parameters and defects on mechanical *** work demonstrates that the micro-tensile sample with small lamellar thickness and large aspect ratio possesses excellent strength and ductility when the loading direction is parallel to the long side of lamellar grain *** finding is helpful to the design of metallic material microstructure with superior com-prehensive *** one hand,the reason for high strength is that the strength increases with the decrease of lamellar thickness according to the Hall-Petch ***,initial dislocation density also participates in the strengthening *** the other hand,the deformation mechanisms include dislocation slip,grain rotation,and the effects of grain boundaries on dislocations,jointly contributing to good ductility.
In-situ high-resolution transmission electron microscopy(HRTEM)is performed to investigate the de-formation behavior of hexagonal close-packed rhenium(Re)which is compressed along the*** simulations are also conducted...
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In-situ high-resolution transmission electron microscopy(HRTEM)is performed to investigate the de-formation behavior of hexagonal close-packed rhenium(Re)which is compressed along the<1(1)00>*** simulations are also conducted to better understand the deformation *** types of lattice reorientation are observed during *** first type involves the reori-entation of one lattice by～90° around<11(2)0>,which is accomplished by the formation of an interme-diate face-center-cubic(FCC)phase at the *** transformation sequence can be described as{1(1)00}matrix →{111}FCC →(0001)*** the second type,a new grain is formed but does not satisfy any known twin relationship with the matrix,and an intermediate FCC phase is also *** transfor-mation sequence can be described as{1(1)01}matrix →{111｝FCC →(0001)*** responsible for the observed lattice reorientation and sequential phase transitions are analyzed by conducting lattice correspondence analyses on the simulation *** accommodation is also analyzed to explain the mechanisms for lattice reorientation and the intermediate phase *** results provide new insight into the deformation behavior of HCP metals.
Given a certain number of areas of interest on Earth, the objective is to maximize the information gained from these areas using different sensors on available satellites. Various constraints exist, such as weather co...
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In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do not possess a mechanism to compensate ...
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Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain position and force information, has b...
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The U.S. Army initiated a shift towards electrifying and hybridizing its tactical vehicle fleet in alignment with its Climate Strategy and global automotive trends. Survey findings indicate a general desire by soldier...
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This study proposes a method for generating motion commands by imitation learning and prediction based on a transformer model using a force-based two-channel bilateral control system. This study aims to demonstrate th...
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Thin-film bulk acoustic resonators(FBARs)operating with essentially thickness-extensional mode have been widely used in communication *** this paper,we provide a convenient means for analyzing FBARs with sandwich-laye...
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Thin-film bulk acoustic resonators(FBARs)operating with essentially thickness-extensional mode have been widely used in communication *** this paper,we provide a convenient means for analyzing FBARs with sandwich-layered structure by appropriately neglecting the high-order terms from 3D elasticity ***,for straight-crested waves,an approximate method is proposed,which can accurately describe the dispersion relation near the operating frequency range of an *** the approximation,the optimum lateral size of a 2D model of frame-like FBAR is obtained,and the results are in good agreement with that obtained by commercial FEM software *** approximation is further extended to variablecrested waves in order to analyze the 3D plate models for real *** mode shapes of 3D FBARs with and without frame-like structures are *** results show that the approximation presented in this paper is of sufficient accuracy and can be used as an efficient tool for the analysis and design of FBARs.
A bottleneck in Laser Powder Bed Fusion(L-PBF)metal additive manufacturing(AM)is the quality inconsistency of its *** address this issue without costly experimentation,computational multi-physics modeling has been use...
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A bottleneck in Laser Powder Bed Fusion(L-PBF)metal additive manufacturing(AM)is the quality inconsistency of its *** address this issue without costly experimentation,computational multi-physics modeling has been used,but the effectiveness is limited by parameter uncertainties and their *** propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics *** is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material *** physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge,which are used to establish a validation checkpoint for our *** data visualization with half-normal probability plots,interaction plots and standard deviation plots,is used to assess if the checkpoint is being *** then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable *** yield statistically and phyiscally validated findings with practical implications,emphasizing the importance of parameter interactions under uncertainty,and their relation to the underlying physics of L-PBF.
The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process s...
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The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.
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