Cutting path-dependent machinability of SiCp/Al composite under multi-step ultra-precision diamond cutting

Particle-tool interactions, which govern the synergetic deformation of SiC particle reinforced Al matrix composites under mechanical machining, strongly depend on the geometry of particle position residing on cutting path. In the present work, we investigate the influence of cutting path on the machinability of a SiCp/Al composite in multi-step ultra-precision diamond cutting by combining finite element simulations with experimental observations and characterization. Be consistent with experimentally characterized microstructures, the simulated SiCp/Al composite is considered to be composed of randomly distributed polygonally-shaped SiC particles with a volume fraction of 25vol%. A multi-step cutting strategy with depths of cut ranging from 2 to 10 μm is adopted to achieve an ultimate depth of cut of 10 μm. Intrinsic material parameters and extrinsic cutting conditions utilized in finite element simulations of SiCp/Al cutting are consistent with those used in corresponding experiments. Simulation results reveal different particle-tool interactions and failure modes of SiC particles, as well as their correlations with machining force evolution, residual stress distribution and machined surface topography. A detailed comparison between numerical simulation results and experimental data of multi-step diamond cutting of SiCp/Al composite reveals a substantial impact of the number of cutting steps on particle-tool interactions and machined surface quality. These findings provide guidelines for achieving high surface finish of SiCp/Al composites by ultra-precision diamond cutting.     

基于EWT的离心压气机出口动态压力样本熵分析

以800 kW离心压气机从稳定状态经过过渡过程进入喘振状态时段的出口动态压力为研究对象，采用经验小波变换并结合样本熵特征，分析了系统在不同工况下的复杂特性。首先，在分析系统动态压力波形特征的基础上，采用经验小波变换并结合皮尔逊相关系数进行信号的提取。其次，研究了提取信号的样本熵与系统工作状态变化的关联关系，并讨论了经验小波的分解层数和样本熵的维数对分析结果的影响。最后，通过将白噪声加入原始信号以验证该方法的抗干扰性能。研究结果显示：当系统由稳态进入喘振状态时，系统出口动态压力的样本熵表现出明显的突变特性，其值由0突变至0.7左右。从系统参数的选择角度，样本熵维数的变化对系统特征的分析影响较小。并且，采用该方法抗干扰性能较好。     

Influence of thickness and initial groove angle in M–K model on limit strain of 7B04 by considering through-thickness stress

Marciniack–Kuczinski (M–K) model is widely used to predict material’s forming limit curve (FLC). The prediction of FLC traditionally neglected through-thickness normal stress. However, it cannot be neglected in some forming processes. Much work has been done to study the effect of through-thickness normal stress on FLC with constant through-thickness normal stress or constant ratio of through-thickness normal stress and maximum principal stress. In addition, based on Nakazima test process, the ratio of through-thickness normal stress and maximum principal stress has been derived, which was a function of instantaneous thickness and loading path. Here, initial groove angle in M–K model was not considered. In this paper, uniaxial tension tests and Nakazima tests were performed on 7B04 aluminum alloy. Based on Hill 48 yield criterion and M–K model, the prediction model of FLC was established. The increase of thickness can enhance FLC. Meanwhile, it is necessary to consider through-thickness normal stress and initial groove angle in prediction model. On the left side of FLC, the effect of initial groove angle on FLC is weakened by increasing sheet thickness. On the right side of FLC, the effect of initial groove angle on FLC is strengthened by increasing sheet thickness. On the right side of FLC, the relation between limit strain points with different thicknesses is linear under one certain loading path. Thickness has decisive effect on through-thickness normal stress level and the changing trendy of through-thickness normal stress during calculation is different under different stress condition.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Experimental and numerical investigation on surface quality for two-point incremental sheet forming with interpolator

The unsatisfied surface quality seriously impedes the wide application of incremental sheet forming (ISF) in industrial field. As a novel approach, the interpolator method is a promising strategy to enhance the surface quality in ISF. However, the mechanism for the improvement of surface quality and the influence of interpolator properties on surface roughness are not well understood. In this paper, the influences of process variables (i.e. tool diameter, step size and thickness of interpolators) on the forming process (e.g. surface roughness, forming force and geometric error) are investigated through a systematic experimental approach of central composite design (CCD) in two-point incremental sheet forming (TPIF). It is obtained that the increase in thickness of interpolators decreases the surface roughness in direction vertical to the tool path while increases the surface roughness in direction horizontal to the tool path. Nevertheless, the combined influence between thickness of interpolators and process parameters (tool diameter and step size) is limited. Meanwhile, the placement of interpolator has little influence on the effective forming force of blank. In addition, the geometric error enlarges with the increase of step size and thickness of interpolator while decreases firstly and then increase with an increase in tool diameter. Finally, the influencing mechanism of the interpolator method on surface quality can be attributed to the decrease of the contact pressure due to the increase of contact area with the unchanged contact force. Meanwhile, the interpolator method eliminates the sliding friction on the surface of blank due to the stable relative position between the blank and the interpolator.     

基于遗传-支持向量机的对置活塞二冲程柴油机气口高度优化

以某对置活塞二冲程柴油机为研究对象,基于一维仿真模型,利用遗传-支持向量机的方法,以油耗为优化目标,进行不同转速工况下进排气口高度组合的自动化寻优。结果表明:在1200r/min下,优化的进、排气口无量纲化高度组合为［0.075,0.105］,所得最小油耗为220.32g/(kW·h);对置活塞二冲程柴油机的气口最优高度应随着转速的提高逐渐增大;且在高转速(大于1600r/min)下,排气口最优高度增加趋势更加明显。      

基于伴随方法的单级低速压气机气动设计优化

采用梯度方法对某型4.5级压气机最后级进行气动设计优化研究，梯度由连续伴随方法计算确定，多排伴随方程采用伴随掺混面模型进行数值求解。首先，采用基于经验修正的初步设计方法设计带进口导叶的4.5级低速、低压缩比压气机的原始气动外形。之后，在压气机近失速工况对最后级静子叶片进行伴随气动设计优化，通过优化叶型和安装角降低流动损失，目标函数定义为加权求和形式的熵增和流量偏差，优化中对流量进行约束。最后，开展基于伴随方法的多工况气动设计优化研究，改善两个不同转速条件下最后级的气动性能。优化结果表明，基于伴随方法的多排气动设计优化可以通过改变叶片气动外形提升多排全工况气动性能。     

Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

高温环境下大尺度薄壁结构的电性能优化设计

考虑温度引起的热变形和材料电磁参数变化对天线罩电性能的影响,基于口径积分-表面积分法(AI-SI)和有限元法(FEM),提出了高温环境下天线罩电性能分析方法;进而基于粒子群算法(PSO),建立了计及热效应的天线罩结构电性能优化流程。分别选取常温环境、均匀高温环境和非均匀高温环境三种典型工况,对比研究了温度对天线罩电性能的影响规律,并对天线罩电性能进行了优化,研究结果表明:高温会引起天线罩透波系数的下降和瞄准误差的上升,在天线罩优化设计中应重视温度对电性能的影响;计及热效应的天线罩结构电性能优化方法能显著提升天线罩的透波系数、降低瞄准误差,具有良好的优化效果。