能量耦合模型的飞秒激光烧蚀面齿轮形貌研究北大核心CSCD

飞秒激光精微加工面齿轮材料18Cr2Ni4WA是去除材料的先进制造方法。本文依据烧蚀凹坑的深度与宽度和激光能量密度的关系得到材料的烧蚀阈值和影响重叠率的因素。考虑齿轮材料成分间互温感应效应与多脉冲激光累积效应,建立材料的能量复耦合模型。通过改变激光能量密度和脉冲数,研究飞秒激光烧蚀凹坑及齿面形貌表面的变化规律,得出脉冲数对烧蚀效果影响小,激光能量密度为1.730 J/cm2激光功率为1.9 W脉冲数N=3000进行烧蚀效果最好可得到最优的实际烧蚀面深度为17.604μm。     

基于改进ICP的复杂机械零件测量点云配准方法

点云配准是基于机器视觉进行复杂机械零件三维非接触精密测量的关键环节。针对传统迭代最近点(iterative closest point, ICP)算法对初始位置依赖性强,迭代收敛速度慢,错误对应点对多,难以满足大批量复杂机械零件测量点云配准效率和精度要求的问题,提出了一种基于ISS-FPFH(intrinsic shape signature-fast point feature histogram)特征结合改进ICP的复杂机械零件测量点云配准方法。为了减少点云配准数量,并保留点云表面原来的细微特征,提出了基于重心邻近点的体素滤波器对点云进行下采样预处理。为解决传统ICP算法因合适初始位置难以确定而导致多视角测量点云配准失败的问题,采用了基于ISS-FPFH特征的采样一致性初始配准(sample consensus intial alignment, SAC-IA)算法进行粗配准。为解决传统ICP算法迭代收敛速度慢、错误对应点对多的问题,提出结合法向量夹角约束的点到平面ICP算法进行精配准。以斯坦福大学的bunny点云模型为对象,验证了本文提出方法对噪声点云的鲁棒性。以常见的复杂机械零...     

模块化多电平换流器IGBT模块失效机理和状态监测研究综述

全控型绝缘栅双极晶体管(insulated gated bipolar transistor, IGBT)模块作为模块化多电平换流器(modular multilevel converter, MMC)的核心功率器件,其失效机理研究和状态监测技术对保证MMC的运行可靠性具有重要意义。IGBT模块劣化引起的运行参数失真和内部结构异常将严重影响MMC的工作性能。目前IGBT模块状态监测综述较多,但缺乏对MMC中IGBT模块状态监测的相关总结。首先分析了MMC的结构特性与工作原理。然后根据焊接式和压接式两种类型IGBT的失效机理,总结了IGBT模块的状态监测技术,并补充了MMC子模块中IGBT模块的状态监测方法并进行分析。最后针对目前研究中存在的不足,结合当下的研究现状,展望了未来柔性直流输电系统中IGBT模块状态监测与评估的研究方向。     

Phase equilibria and microstructure development in Mg-rich Mg-Gd-Sr alloys: Experiments and CALPHAD assessment

Mg-Sr alloys are promising to fabricate orthopedic implants. The alloying of rare earth elements such as Gd may improve the comprehensive mechanical properties of Mg-Sr alloys. The information on the phase diagram and the microstructure development are required to design chemical composition and microstructure of Gd alloyed Mg-Sr alloys. The phase equilibria and the microstructure development in Mg-rich Mg-Gd-Sr alloys (Gd, Sr < 30 at. %) are experimentally investigated via phase identification, chemical analysis, and microstructure observation with respect to the annealed ternary alloys. The onset temperatures of liquid formation are measured by differential scanning calorimetry. A thermodynamic database of the Mg-rich Mg–Gd–Sr ternary system is developed for the first time via CALPHAD (CALculation of PHAse Diagram) approach assisted by First-Principles calculations. The thermodynamic calculations with the developed database enable a well reproduction of the experimental findings and the physical-metallurgical understanding of the microstructure formation in solidification and annealing.     

A new force-depth model for robotic abrasive belt grinding and confirmation by grinding of the Inconel 718 alloy

Robotic abrasive belt grinding has been successfully applied to the grinding and polishing of aerospace parts. However, due to the flexible characteristics of robotic abrasive belt grinding and the time-varying characteristics of the polishing contact force, as well as the plastic and difficult-to-machine material properties of Inconel 718 alloy, it is very difficult to control the actual removal depth and force of the polished surface, which brings great challenges to robot automatic polishing. Therefore, the relationship between the grinding force and the grinding depth in the robotic abrasive belt grinding is analyzed in detail, the robot machining pose error model considering the deformation of the grinding head is established, and the Inconel 718 alloy machining experiment of the robotic abrasive belt grinding is designed. The mapping relationship between the grinding force and the grinding depth is obtained, and the grinding force ratio in the downgrinding and upgrinding mode is discussed. The experimental and theoretical comparisons results show that with the increase of the grinding depress depth, both the grinding depth and the grinding force show an irregular increasing trend, and the increasing trend of the grinding force (increases by about 344.44%–445.45%) is obviously greater than that of the grinding depth (increases by about 52.94%). When the grinding depress depth is large (greater than 3 mm), the feed direction force and the normal force appear obvious secondary pressure peaks at the beginning and end of grinding, which has not been seen in previous studies. In addition, regardless of whether it is downgrinding or upgrinding, the grinding force ratio decreases with the increase of the depress depth, and the grinding force ratio of downgrinding (average 0.668) is smaller than that of upgrinding (average 0.724). This study provides a reference for robotic abrasive belt grinding, and the surface quality of Inconel 718 alloy of robotic abrasive belt grinding can be further improved through the optimization of force and depth.     

Thermodynamic assessment of RuO4 oxide

RuO₄ oxide appears much less stable than RuO₂(s) in the Ru–O binary system with a melting point close to room temperature and a certain propensity to vaporize or decompose at low temperatures. Ab initio simulations in the framework of density functional theory (DFT) on RuO₄(s) are performed to analyze the cubic and monoclinic structures and to evaluate the heat capacities at low temperatures. Then, a critical evaluation of thermodynamic data from calorimetry and vapor pressure determinations - was carried out coupled with ab-initio calculations to propose new thermodynamic data: the entropy.S° (RuO_4, s, cubic, 298K) = 132.7 J·K⁻¹mol⁻¹ and formation enthalpy.Δ_fH° (RuO₄, s, cubic, 298K) = −252.4 ± 5.5 kJ mol⁻¹.     

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.     

Probabilistic crack trajectory analysis by a dimension reduction method

This paper proposes a novel analysis method of stochastic crack trajectory based on a dimension reduction approach. The developed method allows efficiently estimating the statistical moments, probability density function and cumulative distribution function of the crack trajectory for cracked elastic structures considering the randomness of the loads, material properties and crack geometries. First, the traditional dimension reduction method is extended to calculate the first four moments of the crack trajectory, in which the responses are eigenvectors rather than scalars. Then the probability density function and cumulative distribution function of the crack trajectory can be obtained using the maximum entropy principle constrained by the calculated moments. Finally, the simulation of the crack propagation paths is realized by using the scaled boundary finite element method. The proposed method is well validated by four numerical examples performed on varied cracked structures. It is demonstrated that this method outperforms the Monte Carlo simulation in terms of computational efficiency, and in the meanwhile, it has an acceptable computational accuracy.