干式铣削Al-50wt%Si高硅铝合金刀具磨损与表面粗糙度评价

本文以Al-50wt%Si高硅铝合金为研究对象，采用单因素试验方法进行无涂层硬质合金刀具干式铣削试验，分析切削参数对刀具磨损和表面粗糙度的影响。结果表明：表面粗糙度受每齿进给量的影响最显著，随每齿进给量的增加而增加，当每齿进给量从0.07 mm/z增加到0.16 mm/z时，表面粗糙度增加2倍；刀具磨损受切削速度的影响最显著，随切削速度的增加而增加，当切削速度从140 m/min增加到260 m/min时，切削总长度降低3倍，而刀具后刀面磨损量仅是260 m/min速度下的0.8倍；表面粗糙度随刀具磨损的增加呈现先增加后降低的变化趋势，切削长度从350 mm增加到1 750 mm，刀具磨损量平均增加4.5倍，而表面粗糙度却下降2倍；硬质合金刀具主要的磨损形式为磨粒磨损、崩刃。     

Effects of input method and display mode of situation map on early warning aircraft reconnaissance task performance with different information complexities

Early Warning Aircraft(EWA) are the main force for air detection and its Human-Machine Interface(HMI) should be designed to support task efficiency and safety. With the application of advanced input method and interface design in EWA, little is known about their actual usability in terms of human factors and ergonomics. The aim of this study was to investigate the effects of the input method and display mode of the situation map on EWA reconnaissance task performance with different information c...     

Improving performance of macro electrolyte jet machining of TC4 titanium alloy: Experimental and numerical studies

Electrolyte jet machining (EJM) is a promising method for shaping titanium alloys due to its lack of tool wear, thermal and residual stress, and cracks and burrs. Recently, macro-EJM has attracted increasing attention for its high efficiency in machining wide grooves or planes. However, macro-EJM generates large amounts of electrolytic products, thereby increasing the difficulty of rapid product removal with a standard tool and reducing the surface quality. Therefore, for enhanced product transport, a novel tool with a back inclined end face was proposed for macro-EJM of TC4 titanium alloy. For comparison, also proposed were ones with a standard flat end face, a front inclined end face, and both front and back inclined end faces. The flow field distributions of all proposed tools were simulated numerically, and experiments were also conducted to validate the simulation results. The results show that one with a 5° back inclined end face can decrease the low-velocity flow zone in the machining area and increase the high-velocity flow zone at the back end of tool, thereby promoting rapid product removal. A relatively smooth bright-white groove surface was obtained. The same tool also resulted in the highest machining depth and material removal rate among the tested ones. In addition, rapid product removal was beneficial to the subsequent processing. Because of its rapid product removal, the machining depth and material removal rate during deep groove machining using the tool with a 5° back inclined end face were respectively 7% and 14% higher than those produced using a standard one. Moreover, the lowest bottom height difference of 0.027 mm can be obtained when the step-over value was 8.2 mm, and a plane with a depth of 0.285 mm and a bottom height difference of 0.03 mm was fabricated using the tool with a 5° back inclined end face.     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.