高体积分数SiCp/Al激光诱导氧化辅助铣削研究

为了解决高体积分数的SiC_p/Al复合材料在常规切削加工中存在切削力大、刀具磨损快、表面完整性差等问题。本文针对高体积分数SiC_p/Al材料开展激光诱导氧化辅助铣削技术研究，通过激光辐照铣削区域形成易于去除的疏松氧化层提高切削性能，同时开展氧化层调控策略及激光诱导氧化辅助下的铣削参数优化工艺研究，研究不同激光能量密度、辅助气体对氧化层质量的影响及铣削参数优化。结果表明随激光能量密度的增大热影响区宽度和烧蚀沟槽深度随之增加，在氧气辅助下易形成疏松且易于去除氧化层。选取较高的激光能量密度可获得较好的氧化效果，而使用PCD金刚石铣刀，主轴转速为10 000 r/min，在每齿进给量为7.5 μm /s可获得最佳表面质量。     

航天器材料应用验证特点及其指标体系设计与优化

“材料应用验证”是为适应复杂工程研制任务而建立的一种材料多参数指标在特定服役需求下应用适用度评估的综合评价方法，也是通过一系列的试验、测试与表征手段获得材料各项性能数据、曲线、图谱，并通过综合分析确定材料应用可行性的分析方法。文章从航天器发展对高性能、多样化材料快速应用转化需求出发，阐释材料应用验证任务具有指标体系的综合性、通用性、短周期、低成本以及闭环式验证特点，进而提出了覆盖性、关重性、精准性、独立性、经济性的指标体系设计原则，以及材料应用验证的三层级五要素即材料批次稳定性、工艺适用性、环境适应性、服役安全性及组件健壮性指标体系设计及优化方法。     

Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

Imbalanced data classification is an important research topic in real-world applications, like fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used to solve this problem. It generates samples according to the distance between minority data. However, the traditional over-sampling method may change the original data distribution, which is harmful to the classification performance. In this paper, we propose a new method called Conditional Self-Attention Generative Adversarial Network with Differential Evolution (CSAGAN-DE) for imbalanced data classification. The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data. In CSAGAN-DE, the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class. Then, the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance. Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method. The results show that the new method can efficiently improve the classification performance compared with other related methods.     

Optimization of multi-element airfoil settings considering ice accretion effect

Almost half of all flight accidents caused by inflight icing occur at the approach and landing phases when high-lift devices are deployed. The present study focuses on the optimization of an ice-tolerant multi-element airfoil. Dual-objective optimization is carried out with critical horn-shaped ice accumulated during the holding phase. The optimization results show that the present optimization method significantly enhances the iced-state and clean-state performance. The optimal multi-element airfoil has a larger deflection angle and wider gap at the slat and the flap compared with the baseline configuration. The sensitivity of each design parameter is analyzed, which verifies the robustness of the design. The design is further assessed when ice is accreted during the approach and landing phases, which also shows performance improvement.     

Effect of gap and overlap on mode Ⅱ interlaminar fracture toughness of automated fiber placement prepreg laminates

Automated Fiber Placement(AFP) technology facilitates the manufacturing process of composite structures with complex geometry owing to its high efficiency and accuracy.However,the unavoidable imperfections induced by the automated layup method bring challenges to the stability of the final mechanical properties of composites.The influence of AFP-induced gaps and overlaps on the mode Ⅱ interlaminar fracture process of oven-cured laminates is experimentally investigated and explicitly revealed.End...     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.