Fabrication of Laser Deposited TiC/Steel Matrix Composite Coatings

The present work investigates the effect of laser scanning beam speeds and the content of TiC in injected powder on morphologies and microstructures of laser deposited beads of a TiC/H13 tool steel composite. The results show that the beam scanning speeds affect the size and morphology of the beads. During laser processing, TiC melts, decomposes, and subsequently, a number of fine TiC precipitates form during cooling that are uniformly distributed in the tool steel matrix. The beam scanning speeds and the amount of injected TiC exert a strong influence on the morphology and size of the fine TiC precipitates. It is believed that the precipitated TiC is the primary phase in hypereutectic Fe-TiC. Rapid cooling develops martensite with retained austenite in a steel matrix. The precipitated TiC can refine grains of the steel matrix as a solidified nucleus. TiC/H13 tool steel composite coatings with various contents of TiC were produced using the laser deposition processing technique.Mechanical Engineerin

Solid Freeform Fabrication Based on Micro-Plasma Powder Deposition

This paper presents a solid freeform fabrication (SFF) technique based on micro-plasma powder deposition (MPPD). The relationship between the geometric features of the deposited layers and the welding parameters is investigated. The arc length is controlled through the monitoring of the arc voltage. The result of building parts with functionally graded components by the MPPD process is shown as well. The microstructure and the properties of the deposited layers are analyzed. The experimental results show that the MPPD process is a promising welding-based solid freeform fabrication technology.Mechanical Engineerin

Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Journal version of arXiv:2010.05300 (NeurIPS 2020). The first two authors contributed equall

Observed Changes of Koppen Climate Zones Based on High-Resolution Data Sets in the Qinghai-Tibet Plateau

Emerging and disappearing climate zones are frequently used to diagnose and project climate change. However, little attempt has been made to quantify shifts of climate zones in Qinghai-Tibet Plateau (QTP) based on the high-resolution data sets. Our results show that highland climate was decreased substantially during 1961–2011 and were mainly replaced by boreal climate. We also found that the mean elevation of boreal and highland climate continues to rise, with obvious longitudinal geographical characteristics over the study period. Furthermore, we found that the climate spaces (a climate space defined as the volume of 10°C × 500 mm here) of both boreal and highland climate types tend to be warm and humid ones, which may provide more suitable climate conditions for species to maintain and promote diversity. Characterization of changes in QTP climate types deepens our understanding of regional climate and its biological impacts.Emerging and disappearing climate zones are frequently used to diagnose and project climate change. However, little attempt has been made to quantify shifts of climate zones in Qinghai-Tibet Plateau (QTP) based on the high-resolution data sets. Our results show that highland climate was decreased substantially during 1961-2011 and were mainly replaced by boreal climate. We also found that the mean elevation of boreal and highland climate continues to rise, with obvious longitudinal geographical characteristics over the study period. Furthermore, we found that the climate spaces (a climate space defined as the volume of 10 degrees C x 500 mm here) of both boreal and highland climate types tend to be warm and humid ones, which may provide more suitable climate conditions for species to maintain and promote diversity. Characterization of changes in QTP climate types deepens our understanding of regional climate and its biological impacts. Plain Language Summary Climate classification is the key to simplifying complex climate and helps to deepen the understanding of regional climate change. Based on the high-resolution data set (LZ0025), the sharp climatic gradient features and their potential biological impact on Qinghai-Tibet Plateau (QTP) was quantified. With the temperature increase, the spatial distribution of highland tundra climate was gradually replaced by boreal climate. More importantly, the contraction of highland climate and the expansion of boreal climate has obvious elevation characteristics. In addition, climate spaces of highland and boreal climate types tend to warm and humid ones, which may provide more climatic niches for different species and contribute to regional biodiversity.Peer reviewe