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Tingjun Wang

Publications and source records attributed to Tingjun Wang.

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Nonlinear Breakdown of Antisymmetric Flexoelectric Coupling

Flexoelectric coupling is conventionally regarded as antisymmetric, such that reversing the strain gradient reverses the polarization direction while preserving its magnitude. Here we report that this antisymmetric coupling breaks down in noncentrosymmetric single crystals driven by large strain gradients, where nonlinearity becomes operative. Harmonic-resolved measurements reveal robust even-order flexoelectric harmonics that are forbidden in antisymmetric systems, as well as the three-wave mixing indicative of symmetry breaking under dual-frequency excitation. These frequency-relevant generations provide evidence for asymmetric flexoelectricity in the nonlinear regime. A reconstructed nonlinear constitutive framework consistently accounts for all observations by incorporating even-order strain-gradient terms. Our findings establish asymmetric flexoelectricity as an intrinsic electromechanical response and uncover a symmetry-breaking mechanism in flexoelectricity.

cond-mat.mtrl-sci

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis. While it does not meet the needs of industrial mass production, because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system with multiple X-ray sources and detectors, which is able to refresh the CT reconstructed slices to the detector frame frequency. This kind of structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects' translation, which makes it easier to be integrated into pipeline. All the detectors are arranged along the conveyor, and observe the objects in different angle of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time to adopt this source-detector layout strategy. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. It is an innovative design for the 4th industrial revolution, providing an intelligent quality inspection solution for digital production.

physics.ins-det