Hot pressing process ameliorates internal defects of PBZ/PVDF composite film for a high electrocaloric effect near room temperature

The poor interface compatibility between inorganic fillers and organic polymer matrix in nanocomposite has presented considerable challenges, which limit the applicable electric field ranges and reduce the interface polarization interaction. In this paper, Pb[Formula: see text]Ba[Formula: see text]ZrO3 (PBZ) nanofibers were introduced into the polyvinylidene fluoride (PVDF) matrix to prepare composite film, and the effect of hot pressing on interface compatibility was investigated at volume composite ratios of 3% and 4%. For the untreated film, [Formula: see text] and [Formula: see text] of the 3 vol.% composite film are 9.68 [Formula: see text]C/cm2 and 401 MV/m, respectively, and those for the 4 vol.% composite film are 9.15 [Formula: see text]C/cm2 and 408 MV/m, respectively. These differences are mainly due to the impact of internal defects. After hot pressing, [Formula: see text] and [Formula: see text] for the 3 vol.% composite film became 10.22 [Formula: see text]C/cm2 and 490 MV/m, respectively. Those for the 4 vol.% composite film are 9.85 [Formula: see text]C/cm2 and 485 MV/m. Experiment and simulation results showed the beneficial effect of hot pressing, which ameliorated poor interfacial compatibility, reduced internal defects, and improved the crystallinity of the composite film. A high electrocaloric effect (ECE) was obtained by using the direct measure method. At −30[Formula: see text]C, the [Formula: see text] values of hot-pressed PBZ/PVDF film at 3[Formula: see text] and 4[Formula: see text] vol.% were 23.81 and 19.73 K, respectively. When temperature increased to 70[Formula: see text]C, the [Formula: see text] values were 9.44 and 7.01 K, respectively, which were 1.58 times of the values of a non-hot-pressed film. These results indicated that hot pressing alleviated the interface problem and resulted in high EC performance under a high-strength electric field.

Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain

The impact elastic wave method (IEW) has been applied to evaluate the thickness and internal defects of the target structure based on the dominant frequency of the response wave that is formed by the repeated reflections in the thickness direction. However, it is difficult to evaluate the size and position of the defect by IEW if the size and depth are relatively small and deep, respectively, and further, it is known that the technique is inapplicable if the target is not a plate-like structures. Therefore, the authors propose a new technique that uses Difference value as a new evaluation index to overcome the limitations of the conventional methods. Difference value shows the change of the response waveform in the time domain; it is computed by using a response waveform of the structures in sound condition as a reference. In this paper, the practicality of the Difference value is investigated by performing experiments using concrete specimens. The results of the experiments demonstrate that Difference value changes by the influence of internal defects, and Difference value evaluates the location of the relatively small defect that is difficult to evaluate by the conventional technique.

Correlation between surface texture and internal defects in laser powder-bed fusion additive manufacturing

The availability of an in-situ monitoring and feedback control system during the implementation of metal additive manufacturing technology ensures that high-quality finished parts are manufactured. This study aims to investigate the correlation between the surface texture and internal defects or density of laser-beam powder-bed fusion (LB-PBF) parts. In this study, 120 cubic specimens were fabricated via application of the LB-PBF process to the IN 718 Ni alloy powder. The density and 35 areal surface-texture parameters of manufactured specimens were determined based on the ISO 25,178–2 standard. Using a statistical method, a strong correlation was observed between the areal surface-texture parameters and density or internal defects within specimens. In particular, the areal surface-texture parameters of reduced dale height, core height, root-mean-square height, and root-mean-square gradient demonstrate a strong correlation with specimen density. Therefore, in-situ monitoring of these areal surface-texture parameters can facilitate their use as control variables in the feedback system.

Regularities of metal flow and defects transformation during rolling in roughing stands of a universal rail and structural mill

Experimental studies carried out in the conditions of a laboratory rolling mill have determined the regularities of the processes of metal flow and roll-out defects of billets during deformation in roughing stands of a universal rail and structural mill. In relation to the box size and gauges types "lying trapeze" and "trapeze", we have determined a significant irregularity of drawing coefficients of the surface layers by roll length and width, as well as the irregularity of drawing in the cross-section of the roll during rolling. It is shown that during deformation the surface zones adjacent to the ends of the roll are subjected to the greatest drawing, and dependence of irregularity of the drawing coefficients over the cross-section of the roll on the shape of the deformation zone has a distinct power-law character. We have established a significant effect of the drawing coefficient, as well as the location and spatial orientation of the billet defects, while the geometric dimensions of the defects don't have such influence on their roll-out coefficients. According to the obtained data, the defects located on the rolling edges are rolled out most intensively both in depth and width, and the transverse defects are rolled out the least intensively. At the same time, the rollability of any defects increases with the growth of drawing coefficient. It is determined that near the side edges of the roll there is an increase in the width (disclosure) of transverse and inclined defects relative to the rolling axis, as well as the disclosure of defects occurs at the end sections of the roll in relation to longitudinal defects. For internal defects, it was found that, similar to surface defects, an increase in the drawing coefficient during rolling contributes to an increase in their roll-out, while the rollout coefficient of internal defects in absolute value is significantly lower than this indicator for surface defects. It was determined that the minimum roll-out coefficient of internal defects occurs when they are located in the core of the sample, while the roll-out coefficient of such defects increases linearly when moving towards the roll surface. The influence of the location, spatial orientation, and drawing coefficient on the rollability of surface and internal defects is generalized in the form of regression equations. It makes it possible to use them in practice to predict the quality of finished rolled metal when changing rolling modes.

Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.