Metal additive manufacturing is an important branch of AM, which provides an effective method for the innovative manufacturing of metal parts. Here, flow chart and main techniques of metal additive manufacturing are firstly described according to the used material types. Many application examples of metal additive manufacturing are then listed based on application value. The summary is finally given to point development direction of metal additive manufacturing in the future. Additive manufacturing, which is an effective supplement to traditional methods, will play an important role in intelligent and digital manufacturing.
Outlier detection is an important task in data mining and many technologies have been explored in various applications. However, due to the default assumption that outliers are non-concentrated, unsupervised outlier detection may not correctly detect group anomalies with higher density levels. As for the supervised outlier detection, although high detection rates and optimal parameters can usually be achieved, obtaining sufficient and correct labels is a time-consuming task. To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy. First, we propose a novel detection model Dual-GAN, which can directly utilize the potential information in identified anomalies to detect discrete outliers and partially identified group anomalies simultaneously. And then, considering the instances with similar output values may not all be similar in a complex data structure, we replace the two MO-GAN components in Dual-GAN with the combination of RCC and M-GAN (RCC-Dual-GAN). In addition, to deal with the evaluation of Nash equilibrium and the selection of optimal model, two evaluation indicators are created and introduced into the two models to make the detection process more intelligent. Extensive experiments on both benchmark datasets and two practical tasks demonstrate that our proposed approaches (i.e., Dual-GAN and RCC-Dual-GAN) can significantly improve the accuracy of outlier detection even with only a few identified anomalies. Moreover, compared with the two MO-GAN components in Dual-GAN, the network structure combining RCC and M-GAN has greater stability in various situations. The experiment codes are available at: https://github.com/leibinghe/RCC-Dual-GAN.
According to the characteristics and cutting requirements of the compressor impeller, such as low rigidity, easy to produce deformation and vibration in machining process, the high speed machining technology was adopted to reduce time, the virtual manufacturing technology was used to solve processing problems in computer before the trial machining and improved programming speed and other key supporting technologies were adopted. The study shows that this green processing of impeller had high machining efficiency, good surface roughness and product quality, low production cost and light environmental pollution. It accords with modern green machining development trend.
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