航天器表面充电效应及其工程计算

文章较全面地介绍了航天器与空间等离子体发生的表面充电效应。从航天器表面充电的发现到充放电带来的危害,阐述了航天器表面充电研究对航天器安全飞行的重要性,最后对如何消减表面充电效应带来的危害提出了建议,分析了用充电软件进行工程模拟计算的可行性.     

钢铁低温发兰,磷化液的研究

发兰，磷化是钢铁表面处理应用最为广泛的工艺。该研究得出了最新工艺配方，使发兰，磷化工艺具有低温性，低渣量，快速性，低成本的特点。     

GFRP铣槽加工铣削力及表面粗糙度研究

铣槽是复合材料较常用的机械加工形式之一。为研究玻璃纤维增强复合材料(Glass Fiber Reinforced Plastic,GFRP)铣槽加工中各加工参数对铣削力和表面粗糙度的影响,开展了以主轴转速、进给速度和轴向切深为因素的正交试验,研究铣削力和表面粗糙度的变化规律。并用Matlab软件对铣削力和表面粗糙度的试验数据进行回归分析,建立了铣削力和粗糙度的经验公式,最后通过试验验证铣削力和表面粗糙度经验公式的正确性。结果表明:轴向切深对铣削力影响最大,其次是主轴转速,进给速度对铣削力影响最小;主轴转速对粗糙度的影响最大,其次是进给速度,轴向切深对粗糙度的影响最小。试验结果与铣削力、表面粗糙度经验公式算得的数值对比,误差范围都在20%以内。     

原位自生TiB_2/Al复合材料磨削表面质量研究

原位自生TiB_2/Al复合材料具有密度小,比强度高,比模量大等特点,在航空航天领域具有广泛的应用前景。为探索原位自生TiB_2/Al复合材料的磨削加工性能,选用单晶刚玉SA砂轮、白刚玉WA砂轮和CBN砂轮在不同磨削参数下对TiB_2/Al复合材料进行磨削试验。首先研究了砂轮材质、转速、工件速度、磨削深度对工件表面粗糙度的影响规律;其次通过对工件表面形貌、磨屑形态、砂轮磨损的观测分析,探索了原位自生TiB_2/Al复合材料磨削表面成形机制;最后基于试验数据,给出了TiB_2/Al复合材料磨削工艺参数优选域。本研究可为颗粒增强金属基复合材料磨削加工提供基础理论支撑。     

TiAlN 涂层钻头钻削钛合金的实验研究

采用Ti Al N涂层硬质合金麻花钻对钛合金(TC4)进行钻削试验,研究了切削用量对轴向力、加工孔壁表面粗糙度和切屑形态的影响。结果表明:轴向力随着进给量的增加而增加,轴向力随着转速的增加而减小。运用指数公式模型对轴向力实验结果进行回归分析,得到轴向力与转速以及进给量之间的关系式,并对该方程进行了检验验证误差均小于7%;随着进给量的增加断屑能力逐渐增加,切屑长度逐渐变短。在低转速和进给量为50 mm/min时切屑形态为短螺旋形切屑,此时排屑和孔壁粗糙度均为最佳。     

星用介质材料表面充放电效应试验研究

航天器表面介质材料易遭受表面充放电危害。利用30keV单能电子对几种不同的航天介质材料进行了表面充放电模拟试验,测量了不同电子通量辐照下的表面充电电位以及放电脉冲。试验结果表明,聚酰亚胺薄膜在接地处理不当时表面可充至千伏以上,易发生表面放电,且辐照强度越大,放电频率越高。表面镀铝的聚酰亚胺薄膜在不接地时,铝膜成为悬浮导体更加剧了放电的危害。而通过渗碳处理的聚酰亚胺薄膜,其良好的导电性能可有效抵御nA/cm~2量级电子的表面充电。聚四氟乙烯天线罩表面未进行防静电处理时,表面充电电位可达万伏量级,极易发生放电。     

表面粗糙度的光学测量方法

表面粗糙度的光学测量方法,具有非接触、精度高和响应快等优点,致使研究十分引人注目,发展极快。本文简单介绍了几种典型的光学测量原理和方法及一些商品化的仪器。     

Experimental study of effect of post processing on fracture toughness and fatigue crack growth performance of selective laser melting Ti-6Al-4V

For Ti-6Al-4V, a titanium alloy increasingly used in aerospace structure, selective laser melting (SLM) is an attractive additive manufacturing technology, which is attributed to its complex construction capability with high accuracy and good surface quality. In order to obtain qualified mechanical properties, SLM parameters and post processing should be tailored for diverse service conditions. Fracture toughness and fatigue crack growth (FCG) behavior are critical characteristics for damage tolerance evaluation of such metallic structures, and they are affected by post processing technologies significantly. The objective of this study is to obtain the fracture toughness and fatigue crack growth behavior of Ti-6Al-4V manufactured by SLM, and to evaluate the influence of post-SLM thermomechanical treatment and surface machining. Fracture toughness and FCG tests were performed for SLM Ti-6Al-4V in three types of post processing status: as-built, heat treated and hot isostatically pressed (HIPed), respectively. Specimens with as-built and machined surface were tested. The microstructure and fractography were analyzed as well in order to investigate the relevance among manufacture process, microstructure and mechanical properties. The results demonstrate that as-built SLM Ti-6Al-4V presents poor ductility and FCG behavior due to martensitic microstructure and residual stresses. Both heat treatment and hot isostatic pressing improve the plane-stress fracture toughness and FCG performance considerably, while surface machining shows slight effect.     

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan,Iran, (Mw = 6.4) earthquake of 11 August 2012

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (M_w = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.