The objective of the present study was to investigate the effects of Sn addition on the mechanical and corrosion properties of Mg-1Zn-1Zr-xSn (x = 1, 2, 3, 4, 5 wt.%) alloys prepared by powder-in-tube rolling (PTR) method. The PTR-treated Mg alloys reached 98.3% of theoretical density. The hardness of the alloy increased with Sn addition. Two main intermetallic phases, Mg2Sn and Zn2Zr3, were formed in the alloys. The Mg2Sn intermetallic particles were observed along the grain boundaries, while the Zn2Zr3 particles were distributed in the Mg matrix. The addition of 1 wt. % Sn caused the corrosion potential to shift toward a more positive value, and the resulting alloy exhibited low corrosion current density.
Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties
The study proposed the model of “guide mark” defects formation on the internal surface of pipes, produced on PRM mills of PRP – 140. The research of pipe forming at plug rolling mill with stub mandrel has been carried out; regularities of the dimensionless parameters characterizing the deformation of the gap release, depending on the reduction ratio, were determined. The model of “guide mark” defect formation on the internal surface of the pipe has been proposed. This allows for lesser wall thickness variation of rough tubes. It has been shown that, when using dioctahedral pass designs in comparison with hexagonal pass designs the proportion of displaced volume along the pipe axis is greater but the value is lower; thereby, the risk of “guide mark” defect forming is reduced.
At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance. Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection.