一种基于多步回溯算法的铣削稳定性预测方法

在加工过程中,由于薄壁件的弱刚性易发生加工颤振,从而对工件表面质量和刀具寿命等造成不良的影响,对铣削过程的稳定性进行预测是至关重要的。通过提出一种多步回溯算法来预测铣削过程的稳定性,将铣削过程离散化成时滞周期方程,在每个时间间隔上采用多步回溯的方法来近似时间周期及时滞项。通过构建状态转移矩阵,根据Floquet理论获得了铣削稳定性边界参数。最后,通过仿真对比实例验证了算法的计算精度和收敛率。结果表明,多步回溯算法具有快速收敛及高计算精度等特点,尤其在低速铣削的稳定性预测中具有良好的应用前景。     

Reliability analysis of structures using neural network method

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical procedure, such as finite element method. To reduce the computational effort required for reliability analysis, response surface method could be used. However the conventional response surface method is still time consuming especially when the number of random variables is large. In this paper, an artificial neural network (ANN)-based response surface method is proposed. In this method, the relation between the random variables (input) and structural responses is established using ANN models. ANN model is then connected to a reliability method, such as first order and second moment (FORM), or Monte Carlo simulation method (MCS), to predict the failure probability. The proposed method is applied to four examples to validate its accuracy and efficiency. The obtained results show that the ANN-based response surface method is more efficient and accurate than the conventional response surface method.     

Prediction of machining chatter in milling based on dynamic FEM simulations of chip formation

Chatter vibration is a major obstacle in achieveing increased machining performance. In this research, a finite element model of chip formation in a 2D milling process is used to predict the occurrence of chatter vibrations, and to investigate the effects of various machining parameters on this phenomenon. The dynamic properties of the machine tool at the tool tip are obtained based on experimental modal analysis, and are used in the model as the cutter dynamics. The model allows for the natural development of vibration as the result of the chiptool engagement, and accounts for various phenomena that occur at the chip-tool interface ultimately leading to stable or unstable cutting. The model was used to demonstrate the effects of the machining parameters, such as the axial depth of cut, radial immersion, and feed rate, on the occurrence of chatter. Additionally, the phenomenon of jumping out of the cut region could be observed in this model and its effect on the chatter process is demonstrated. The numerical model is verified based on comparisons with experimental results.The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-018-0228-7.pdf     

基于IMB-CNN的薄壁件超声铣削颤振辨识方法

针对薄壁件超声铣削加工时产生的颤振严重影响工件质量,加剧刀具磨损的问题,搭建了颤振图像监测系统,利用卷积神经网络(CNN)进行颤振图像辨识,综合运用趋磁细菌算法(MB)、爬山算法(HC)和禁忌算法(TS)的优点,改进MB算法进行超参数优化,提出了一种基于改进趋磁细菌卷积神经网络(IMB-CNN)的薄壁件超声铣削颤振辨识方法。首先,通过MB算法进行全局搜索,再以最优解为初始点,通过HC算法进行邻域搜索,避免了MB算法在最优解附近的振荡;同时,通过禁忌列表跳过已搜索的节点,减小计算规模,加快计算效率;最后,将获得的最优超参数用于CNN,实现颤振图像的精确辨识。与其他方法相比,该方法实现了97.69%的识别率,判断时间为363ms,能有效地进行颤振监测,且整体性能较优。     

基于动力学及切削特性耦合的数控机床结构设计

切削性能是评价数控机床好坏的重要指标之一,在结构设计阶段必须加以考虑。以提高实验室自主研发的立卧转换四轴联动数控机床的切削性能为目的,研究基于动力学特性与切削特性耦合的机床结构设计优化方法。首先结合切削加工中的颤振稳定域理论,通过实验获取机床刀尖频响函数和切削力系数,预测切削加工时的三维颤振稳定域图(主轴转速-切宽-切深)及颤振频率图(主轴转速-切宽-频率)。其次采用实验模态技术对整机进行结构动力学测试及分析,在获取可视化振型的基础上,分析引起机床发生颤振,并导致切削性能降低的结构设计上的缺陷。在此基础上,改进主轴头结构并比较刀尖频响函数,结果表明系统动刚度约增加28.2%,机床抵抗切削颤振的能力得到明显加强。     

铣削过程颤振稳定性分析的研究进展

综述铣削过程颤振稳定性分析的研究概况和进展。颤振建模和稳定性分析是该方法两个关键环节。依据颤振形成的物理条件,将其分为摩擦型颤振、振型耦合型颤振和再生型颤振。从切削过程的非线性和切削系统的非线性两方面,重点介绍再生型颤振的非线性建模的研究成果。稳定性分析方法根据对颤振模型的求解方法,分为频域法、离散法及数值法,概括了各个方法的特点、效果及适用工况。最后介绍了近来兴起的微细铣削研究领域中颤振稳定性分析的研究成果。由于其尺度效应,微细铣削加工具有独特的加工机理和特点,颤振建模中需考虑的因素与传统铣削多有不同,但稳定性分析方法仍大多沿用传统铣削中的方法。     

Vibration Suppression of Thin-Walled Workpiece Machining Based on Electromagnetic Induction

Thin-walled workpieces are largely manufactured in the aerospace industry. The manufacturing process has been a problem due to its flexibility, and chatter vibrations are apt to occur, which restricts the machining efficiency and quality. A vibration suppression device for thin-walled workpieces is presented based on the electromagnetic induction principle, which utilizes machining vibrations to generate resistant force on the workpiece. The formulated force varies with the workpiece vibration velocity, but in an opposite direction. Excitation tests using the electromagnetic shaker illustrate that the device is effective in vibration attenuation. Finally, machining tests are carried out with applications to two thin-walled structures for further verification. The machining vibrations and surface quality demonstrate the damping promotion of the workpiece assembly, and milling stability limit is increased by more than twofold.     

Controlling vibrations of a cutting process using predictive control

Unwanted vibrations in machining are detrimental to the equipment and the quality of the result. Notably chatter vibrations due to the regenerative effect are difficult to control and limit the achievable results. Typically, active and passive means are employed to prevent chatter from happening. This work proposes a predictive control strategy that actively uses information about the system past to predict future disturbances. Using those predicitions allows to counter the regenerative effect more effectively. The strategy is tested in simulation and improves the dynamic stability of the system greatly. It is robust with respect to quantitative errors in the disturbance predictions.     

基于轴对称非球面元件的加工模型研究

轴对称非球面元件具有优良的光学性能,在现代光学系统中的应用占有越来越大的比例,对其加工质量和加工精度的高要求给光学元器件的制造业带来更大的挑战。针对轴对称非球面的精密磨削加工系统中的金刚石砂轮加工要求,提出了合理的原理方案,给出了合适的加工模型,为平面砂轮加工非球面元件提供了可行的技术方案。     

薄壁铣削加工颤振稳定性研究综述

针对薄壁铣削过程中的颤振问题。论述了薄壁铣削加工再生型颤振理论模型的研究进展;深入探讨颤振的根源—铣削力模型,对比分析薄壁铣削力经验模型、有限元模型及解析模型的优缺点;对薄壁铣削系统动态特性进行详细论述;刀尖频响函数求取法分为试验法、模态综合法和动柔度子结构耦合法;薄壁件动态特性研究法分为试验法、有限元法及解析法,详细论述了各种方法的原理、研究现状及优缺点。详细对比分析了频域法和时域法这两种稳定性求解方法,对介观尺度薄壁微铣削颤振稳定性相关研究进行探讨;并对薄壁铣削加工颤振稳定性研究现状和后续研究方向进行归纳和探讨。     

铣削颤振稳定域叶瓣图确定方法研究

铣削颤振是一种非稳定性振动,会损伤工件表面质量,降低切削效率。本文提出了通过理论模型计算与实验设计相结合方法确定铣削颤振稳定域叶瓣图。基于实验测量数据和理论分析计算,利用遗传算法来优化确定各常数参数,从而绘制出与实验测量结果相一致的铣削颤振稳定域叶瓣图。该叶瓣图可有效应用到该机床实际铣削加工过程中的参数选择,对提高铣削加工效率和加工质量有重要意义。     

基于贝叶斯理论的非线性结构模型修正及其动力可靠度分析EI北大核心CSCD

提出一种基于贝叶斯推理的非线性结构模型修正方法,同时考虑激励的随机性,建立了复合随机振动系统的动力可靠度分析方法。利用实测结构动力响应主分量的瞬时特征参数作为非线性指标构建似然函数,结合拒绝延缓自适应(Delayed Rejection and Adaptive Metropolis,DRAM)算法和高斯过程替代模型实现了非线性结构模型修正及其参数的不确定性量化。根据首次超越破坏准则,利用广义概率密度演化方法,分别对仅考虑激励随机性的确定性模型和同时考虑结构参数与激励不确定性的复合随机振动模型进行动力可靠度分析,并利用蒙特卡洛随机抽样方法验证了计算结果的准确性。研究结果表明:基于振动响应瞬时特征参数的贝叶斯推理方法能够快速、准确地实现结构的非线性模型修正及其参数的不确定性量化。与具有初始设计参数名义值的确定性模型相比,考虑参数不确定性的复合随机模型的动力可靠度总体偏低,因此,在结构安全评估中应考虑非线性模型参数不确定性的影响,使评估结果更加安全、可靠。     

A Failure Mode Effect and Criticality Analysis of Conventional Milling Machine Using Fuzzy Logic: Case Study of RCM

The purpose of this paper is to select the appropriate maintenance strategies for each failure mode of functionally significant item of conventional milling machine. In order to describe the criticality analysis of conventional milling machine, this paper presents a study on reliability‐centered maintenance with fuzzy logic and its comparison with conventional method. On the basis of fuzzy logic, failure mode and effect analysis is introduced integrating with fuzzy linguistic scale method. After that, weighted Euclidean distance formula and centroid defuzzification is used for calculating risk priority number. The results indicate that based on risk priority number, value criticality ranking was decided, and appropriate maintenance strategies were suggested for each failure mode. It also reflects that a more accurate ranking can be performed by the application of fuzzy logic using linguistic rule to failure mode and effect analysis. Copyright © 2016 John Wiley & Sons, Ltd.     

基于蒙特卡罗模拟的高效边坡可靠度修正方法

传统的蒙特卡罗模拟方法在分析由于参数不确定性修正而引起的可靠度修正问题时效率较低。为此,提出了一种基于蒙特卡罗模拟的高效边坡可靠度修正方法,该方法主要包括2个关键步骤:1)根据参数初始分布利用蒙特卡罗模拟方法计算边坡的失效概率,并输出蒙特卡罗模拟的失效样本;2)利用参数统计特征值修正后的联合概率密度函数和蒙特卡罗模拟失效样本计算修正后边坡的失效概率。以两个边坡问题为例说明了所提方法的有效性。结果表明:所提出的方法在计算修正的失效概率过程中无需重新执行蒙特卡罗模拟,计算过程简单、计算效率高。此外,所提方法能够适用于隐式表达功能函数的边坡可靠度修正问题,并能够有效地解决单变量和多变量修正的边坡可靠度修正问题。     

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feed-forward multi-layered artificial neural network (ANN) roughness prediction model, using the Levenberg-Marquardt backpropagation training algorithm, is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials. Milling experiments were conducted on a computer numerical control (CNC) milling machine with polycrystalline diamond (PCD) tools to acquire data for training the ANN roughness prediction model. Four cutting parameters were considered in these experiments: cutting speed, depth of cut, feed rate, and volume fraction of SiC. These parameters were also used as inputs for the ANN roughness prediction model. The output of the model was the average surface roughness of the machined workpiece. A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters, with a 2.08% mean relative error. Moreover, a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91% mean relative error was developed based on the ANN roughness prediction model. Finally, a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00326-x     

基于自组织特征映射神经网络的零件加工尺寸类型识别

 提出一种基于自组织特征映射神经网络（SOFM）的零件加工尺寸类型识别方法.首先从三维CAD软件中获取包含零件特征数据的XML文件,并从文件中提取相应的加工特征及尺寸.然后以零件加工特征作为SOFM的输入层的神经元,经处理后作为SOFM的输入向量,利用SOFM自学习和自组织能力对输入向量进行训练.训练好的网络可以实现对零件加工尺寸类型进行较好的识别.最后通过对某零件的尺寸类型识别,验证了所提方法对平面、内孔、外圆和定位四类典型加工尺寸类型识别的有效性.     

Probabilistic fracture mechanics analysis of linear-elastic cracked structures by spline fictitious boundary element method

The inherent uncertainties in crack geometry, material properties and loadings have large influence on fracture response characteristics of cracked structures. This paper presents the probabilistic fracture mechanics analysis of linear-elastic cracked structures subjected to mixed-mode loading conditions using the spline fictitious boundary element method (SFBEM). The response surface method (RSM) is used to predict the fracture probability of the cracked structure. To determine the unknown coefficients of the response surface function, the SFBEM based on the Erdogan fundamental solutions for infinite cracked plates is adopted to perform deterministic analyses of stress intensity factors (SIFs) corresponding to different test points with given parameters. Numerical examples based on mode-I and mixed-mode crack problems are presented to illustrate the present method. The results show that the predicted failure probability obtained by the present approach is accurate in comparison with the Monte Carlo simulation (MCS) results. Since a much lesser number of numerical tests are required in RSM as compared with that needed in MCS, and since the SFBEM based on the Erdogan fundamental solutions has been used to conduct the numerical tests, reliability analysis of cracked structures can be performed efficiently using the present method.     

基于小波包变换与核主成分分析的铣削颤振识别

提出一种基于小波包变换（wavelet packets transform, WPT）与核主成分分析（kernel principal component analysis,KPCA）的颤振识别方法。铣削颤振会抑制或增强某些频段内的信号,利用四层小波包分解与重构,得到16个频段内的重构信号,获得各重构信号的面积,并进行归一化处理,完成铣削颤振特征向量的选择。继而通过对比基于主成分分析（principal component analysis,PCA）与核主成分分析的特征提取方法的特征提取效果,选取KPCA对特征向量进行降维处理,最后以降维后的数据作为最小二乘支持向量机分类器的输入对铣削状态进行识别。结果表明,在小样本的情况下仍能有效、准确地对铣削状态进行分类,分类准确率达95.0 %。     

基于犹豫模糊决策的铣削参数优化

为提升铣削加工质量，研究一种基于犹豫模糊决策的数控铣削参数优化方法。根据铣削过程机理和实验数据建立铣削参数优化的数学模型，将犹豫欧氏距离与模糊逻辑推理相结合，对铣削过程中多响应系统进行简化，既避免了传统模糊测度方法中权重的设置，也充分提取了各响应之间相关性的有效信息，最后通过对实验中可控因子与模糊推理过程输出值进行主效应分析，得到铣削过程控制因子的最佳参数组合：当进给量为0.01 mm/tooth，铣削深度为0.064 mm、铣削速度达到396 m/min，铣削宽度达到12.26 mm时，加工零件的表面粗糙度R_a和R_t可以得到整体优化，从而提升加工零件的质量。该方法首次将犹豫模糊决策理论方法应用于铣削过程工艺参数优化，避免了均值处理法带来的信息损失，可增加实验设计的鲁棒性。与满意度函数法相比，研究的基于犹豫模糊决策的铣削参数优化方法不受权重大小制约，能够同时使过程的两个响应得到优化，具有实用的有效性和可操作性。