Self-repair of structural and functional composites with intrinsically self-healing polymer matrices: A review

Self-healing is a smart and promising way to make materials more reliable and longer lasting. In the case of structural or functional composites based on a polymer matrix, very often mechanical damage in the polymer matrix or debonding at the matrix–filler interface is responsible for the decrease in intended properties. This review describes the healing behavior in structural and functional polymer composites with a so-called intrinsically self-healing polymer as the continuous matrix. A clear similarity in the healing of structural and functional properties is demonstrated which can ultimately lead to the design of polymer composites that autonomously restore multiple properties using the same self-healing mechanism.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling

To minimize the mass and increase the bearing failure load of composite doublelap bolted joints, a three-step optimization strategy including feasible region reduction, optimization model decoupling and optimization was presented. In feasible region reduction, the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses, and the feasible regions of variables were reduced by influence mechanism analyses. In model decoupling, the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis. In the third step, the optimization sub-models were solved one by one using a genetic algorithm, and the modified characteristic curve method was adopted as the failure prediction method. Based on the proposed optimization method, optimization of a double-lap single-bolt joint was performed using the ANSYS^® code. The results show that the bearing failure load increased by 13.5% and that the mass decreased by 8.7% compared with those of the initial design of the joint, which validated the effectiveness of the three-step optimization strategy.     

Tribological behavior of Zr-based bulk metallic glass sliding against polymer,ceramic, and metal materials

The tribological properties of zirconium-based bulk metallic glass (BMG) sliding against polymers, steels, and ceramics at different loads and speeds were investigated. Acoustic emission (AE) technology was used to analyze wear states. The frictional coefficients of the BMG slid against the steel and ceramic balls were high but decreased with increased applied normal load and sliding speed. As the steel balls were more ductile than the ceramic ones, the steel–BMG sliding pairs generated weaker AE signals and exhibited larger wear rates. The BMG tested against the polymer balls had much lower and more stable frictional coefficients than the ones against the steel or ceramic balls because of transferred polymer layers on the BMG surfaces. The BMG against the polymer balls also exhibited the highest AE signals among the three types of counter materials used, indicating that abrasive wear dominated in the polymer-BMG sliding pairs. These results demonstrated the potential application of the BMG as a new tribomaterial that could be an alternative to the traditional crystalline metals for various counter materials.     

Formation of ST12 phase Ge nanoparticles in ZnO thin films

In this work, we focus on the Ge nanoparticles (Ge-np) embedded ZnO multilayered thin films. Effects of reactive and nonreactive growth of ZnO layers on the rapid thermal annealing (RTA) induced formation of Ge-np have been specifically investigated. The samples were deposited by sequential r.f. and d.c. sputtering of ZnO and Ge thin film layers, respectively on Si substrates. As-prepared thin film samples have been exposed to an ex-situ RTA at 600 °C for 60 s under forming gas atmosphere. Structural characterizations have been performed by X-ray Diffraction (XRD), Raman scattering, Secondary Ion Mass Spectroscopy (SIMS), and Scanning Electron Microscopy (SEM) techniques. It has been realized that reactive or nonreactive growth of ZnO layers significantly influences the morphology of the ZnO: Ge samples, most prominently the crystal structure of Ge-np. XRD and Raman analysis have revealed that while reactive growth results in a mixture of diamond cubic (DC) and simple tetragonal (ST12) Ge-np, nonreactive growth leads to the formation of only DC Ge-np upon RTA process. Formation of ST12 Ge-np has been discussed based on structural differences due to reactive and nonreactive growth of ZnO embedding layer.     

Plasmonic-excitonic multi-hybrid glass-based nanocomposites incorporating Ag plus core-shell CdSe/CdS and alloyed CdSxSe1−x nanoparticles

Successful fabrication of glass-based hybrid nanocomposites (GHNCs) incorporating Ag, core-shell CdSe/CdS and CdS_xSe_1?x nanoparticles (NPs) is herein reported. Both metallic (Ag) and semiconductor (CdSe/CdS) NPs were pre-synthesized, suspended in colloids and added into the sol-gel reaction medium which was used to fabricate the GHNCs. During fabrication of the nanocomposites a fraction (20–60%) of core-shell CdSe/CdS NPs was alloyed into CdS_xSe_1?x (0.20 < x < 0.35) NPs without changing morphology. Modulation of in situ alloying is possible via the relative content of organics added into the sol-gel protocol. Within colloids Ag (core-shell CdSe/CdS) NPs presented average diameter and polydispersity index of 49.5 nm (4.2 nm) and 0.41 (0.21), respectively. On the other hand, the Ag (core-shell CdSe/CdS) NPs’ average diameter and polydispersity index assessed from the GHNCs were respectively 51.5 nm (4.1 nm) and 0.43 (0.25), revealing negligible aggregation of the nanophases within the glass template. The new GHNCs herein introduced presented two independent excitonic transitions associated to homogenously dispersed semiconductor NPs, peaking around 420 nm (core-shell CdSe/CdS) and 650 nm (CdS_xSe_1?x) and matching the plasmonic resonance (Ag NPs) in the 400–500 nm range. We envisage that the new GHNCs represent very promising candidates for superior light manipulation while illuminated with multiple laser beams in quantum interference-based devices.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Measuring changing strain fields in composites with Distributed Fiber-Optic Sensing using the optical backscatter reflectometer

Background/purposeMeasurements of strains in critical components are often required in addition to finite element calculations when evaluating a structure.MethodsThis paper describes how standard optical fibers, bonded to the surface or embedded in a laminate, can measure strain fields along the entire length of the fiber, using the optical backscatter reflectometer.ResultsA strain field measurement can be much better compared to simulations than the more common single point measurements with strain gauges or Bragg Gratings. Changes of the strain field can be related to damage development and can be used for structural health monitoring. Practical aspects of using the fibers are also discussed.ConclusionDistributed Fiber-Optic Sensing was successfully embedded and bonded to a composite joint. Adhesive damage was identified and the strain field agreed well with FE-Analysis.     

Dhh1 is a member of the SESA network

The correct separation of chromosomes during mitosis is necessary to prevent genetic instability and aneuploidy, which are responsible for cancer and other diseases, and it depends on proper centrosome duplication. In a recent study, we found that Smy2 can suppress the essential role of Mps2 in the insertion of yeast centrosome into the nuclear membrane by interacting with Eap1, Scp160, and Asc1 and designated this network as SESA (S my2, E ap1, S cp160, A sc1). Detailed analysis showed that the SESA network is part of a mechanism which regulates translation of POM34 mRNA. Thus, SESA is a system that suppresses spindle pole body duplication defects by repressing the translation of POM34 mRNA. In this study, we performed a genome-wide screening in order to identify new members of the SESA network and confirmed Dhh1 as a putative member. Dhh1 is a cytoplasmic DEAD-box helicase known to regulate translation. Therefore, we hypothesized that Dhh1 is responsible for the highly selective inhibition of POM34 mRNA by SESA.