An increasing trend can be seen in the use of CFRP and titanium as hybrid materials in the aerospace industry, but machining them still remains the main challenge due to their anisotropic mechanical ...behavior and poor machinability. Since helical milling, in open literature, has been found to produce better results with CFRP/Ti stack, different milling strategies were experimentally designed, with the main goal of this paper being to obtain the milling strategy with best hole quality. The key factors monitored during the experiments were milling order, thrust force, temperature, and hole quality. The four strategies included the first two, with milling the 12 mm hole diameter in one attempt; the other two, with a three-step milling process, while all the strategies differ from each other in terms of machining order of the workpiece. It is showed that thrust forces are linearly proportional to the feed rates and inversely proportional to the cutting speeds. Among the first two strategies, the CFRP/Ti order stack experienced less damage compared to the Ti/CFRP one. However, the three-step milling definitely brought better hole quality results in terms of significant reduction in delamination, and to further prove this point, SEM scans were essential for delamination factors to be calculated for each strategy. Even though the delamination factor was lower in the three-step milling, the order of the stack also played a major role in reducing the degree of delamination. When starting with CFRP as the top layer, the titanium plate acts as a support to prevent push-down delamination in CFRP.
Memristor with low‐power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device ...characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration‐based memristor is expected to provide a compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, high‐performance memristor based on the structure of ITO/Ni single‐atoms (NiSAs/N‐C)/Polyvinyl pyrrolidone (PVP)/Au is reported. By utilizing well‐distributed trapping sites , small tunneling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, ultralong retention capability of 106 s, a low set voltage (Vset) of ≈0.7 V, a substantial ON/OFF ration of 103, and low spatial variation in cycle‐to‐cycle (500 cycles) and device‐to‐device characteristics (128 devices) is demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. Finally, a memristor ratioed logic‐based combinational memristor array to realize the one‐bit full adder is further implemented.
In this paper, a novel Ni single‐atoms (NiSAs/N‐C) based non‐volatile memristor to address the contradictory issue of improving both switching speed and retention capability is designed. The device exhibits an ultrafast switching speed of 100 ns and ultralong data retention capability (106 s). After fitting the Arrhenius equation to achieve a data retention lifetime over 10 years.
Ghosting artifacts caused by moving objects and misalignments are a key challenge in constructing high dynamic range (HDR) images. Current methods first register the input low dynamic range (LDR) ...images using optical flow before merging them. This process is error-prone, and often causes ghosting in the resulting merged image. We propose a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images. Unlike previous methods that directly stack the LDR images or features for merging, we use dual-attention modules to guide the merging according to the reference image. DAHDRNet thus exploits both spatial attention and feature channel attention to achieve ghost-free merging. The spatial attention modules automatically suppress undesired components caused by misalignments and saturation, and enhance the fine details in the non-reference images. The channel attention modules adaptively rescale channel-wise features by considering the inter-dependencies between channels. The dual-attention approach is applied recurrently to further improve feature representation, and thus alignment. A dilated residual dense block is devised to make full use of the hierarchical features and increase the receptive field when hallucinating missing details. We employ a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images. Although DAHDRNet is not flow-based, it can be applied to flow-based registration to reduce artifacts caused by optical-flow estimation errors. Experiments on different datasets show that the proposed DAHDRNet achieves state-of-the-art quantitative and qualitative results.
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but extensive ...literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach, however, is that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.
The health monitoring for the service status of the composite materials is particularly crucial, calling for highly sensitive piezoresistive strain sensors. This paper outlined the working mechanisms ...of CNT buckypaper sensor during loading and releasing processes by analyzing the electrical conduction mechanism of the adjacent CNT and developing the macroscopical and mesoscopic models of CNT electrical networks inside of the bukypaper. The CNT-based nanocomposite specimens underwent monotonic and cyclic flexural loading in low strain range to examine the dynamic stability and durability of the in-situ nano sensor. The different support span configurations were also employed and operated to obtain the recommended application scenarios. The long-term response behavior of the nano sensor was also concluded as three typical phases. The desirable agreement was demonstrated by comparing the responses of ΔR/R0 signals and phenomena of the breakage and propagation of micro cracks and the rearrangement and reconnection of CNT conductive networks within the Scanning electron microscope (SEM) images of the buckypaper. The proposed flexural nano sensor is suitable for potential health monitoring application of composite materials.
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input low dynamic range ...(LDR) images using optical flow before merging them, which are error-prone and cause ghosts in results. A very recent work tries to bypass optical flows via a deep network with skip-connections, however, which still suffers from ghosting artifacts for severe movement. To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image. The attention modules automatically suppress undesired components caused by misalignments and saturation and enhance desirable fine details in the non-reference images. In addition to the attention model, we use dilated residual dense block (DRDB) to make full use of the hierarchical features and increase the receptive field for hallucinating the missing details. The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error. Experiments on different datasets show that the proposed AHDRNet can achieve state-of-the-art quantitative and qualitative results.
Non-conventional hole-making machining processes are being adopted to manufacture holes in the difficult to cut materials like carbon fiber reinforced polymer (CFRP) composites. Of the many ...non-conventional processes, helical milling remains the most favorable and the best alternative to conventional drilling. It possesses several advantages including low cutting forces, reduced tool wear and improved hole quality. The use of composites in conjunction with metals is very common in aerospace industry and the most common configurations are CFRP/Al and CFRP/Ti-6Al-4V. In this work, a two step process for helical milling in CFRP/Al stack is proposed and experimented then the results are compared with that of conventional drilling. In the first step, milling is done on the CFRP/Al stack starting on the composite part then the workpiece is flipped so that in the second step milling starts on the metal part. Results show that helical milling reduces axial forces by about 35% compared to that of conventional drilling. The forces involved in the second step were found to be about 25% less than that of the first step in the helical milling process. Reduction in forces consequently leads to less damage as scanning electron microscope (SEM) images of the machined holes show.
Currently, most designs for interlayer toughening of carbon-based filler/polymer nanocomposites are highly dependent on experimental iterative trial and error, and there is no rational design ...framework. This work uses machine learning to build a fast and accurate predictive model and assess the extent to which key features affect performance, giving researchers ideas for designing new materials and greatly improving efficiency. A training database is built by first collecting the features of the domain that affect the interlaminar performance. A stacking model fusion of the three machine learning models was then performed to construct a highly accurate fast prediction model. Besides, the importance of key features is evaluated during model training using the Random Forest Algorithm (RFA). Finally, by predicting the performance of materials and analyzing the importance of characteristics to guide material preparation, the development cycle is shortened and costs are reduced.
•A model was able to predict the GIIC value of the laminate with an R2 value of 0.916.•Several guidelines for the design of carbon matrix composite laminates.
The carbon nanotube (CNT) and graphene oxide are widely applied in Glass Fiber Reinforced Polymer (GFRP) to enhance interlaminar performance, even though they have an extremely expensive price. ...Herein, we provide a novel interlaminar toughening material: carbon black, which has a cheap price and good interlaminar properties. The relationship between different amounts of three nanomaterials and toughening efficiency is obtained through experiments (end notch bending (ENF) test) and the curves between different amounts and prices are revealed by the investigation. Moreover, a mathematical model of interlaminar performance, material consumption, and price is established, which can provide researchers with accurate, efficient, and inexpensive predictions in different application environments. On the other hand, a simple and efficient spraying method for making reinforcement layers is adopted in this research and we analyzed the microstructure strengthening mechanism of three nanomaterials by scanning electron microscopy (SEM).
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