机床主轴回转误差对加工精度影响分析

机床的质量取决于机床关键部件的质量,而机床主轴部件是保证机床加工精度的核心,主轴回转误差是影响机床加工精度的重要因素之一,直接影响到加工零件的形状精度、表面的粗糙程度和质量,实验结果表明:由主轴回转误差引起的精密     

主轴回转精度的系统建模与混合谱分析法

一、引言主轴回转精度分析历来是精密圆柱零件加工与测试技术中的重要研究课题。以往在加工设备与量仪的设计与制造上,对影响主轴回转精度的诸因素:轴系零件几何精度、装配精度、工作条件、结构形式等,作了多方面改善和提高。近年来又多致力于分离主轴回转误差的测试技术研究,如多位法、反向法、或两者的复合应用等,以及通过精确检测主轴回转误     

主轴回转误差引入飞切加工表面微波纹的形成机理与解决方案

为分析超精密飞切机床加工表面微波纹的形成机理,研究了主轴回转误差信息提取与表面形貌仿真技术,获取微波纹误差来源并研究解决方案。首先,在超精密飞切机床主轴上搭载五通道在线电容位移检测系统,并对采集到的信号进行误差分析提取。然后,建立飞切加工表面微观形貌三维仿真模型,仿真分析主轴误差引入的加工表面微波纹,并与表面检测结果比对确定误差来源。最后,通过调整主轴电机控制系统抑制该误差。三维仿真和实测结果相吻合,证实超精密飞切机床主轴转速波动导致的回转误差造成了工件表面1 Hz左右的规律性条纹,对主轴转速控制系统进行数字化改造后,基本消除了该因素导致的表面微波纹,表面粗糙度从5 nm以上抑制到2 nm左右,PV值优于10 nm。超精密飞切机床主轴转速波动会对飞切加工表面微观形貌以及表面粗糙度产生显著影响,需至少控制在0.5 r/min以内。     

试谈机床精度对机床加工精度的影响

影响机床加工精度的因素很多,如机床热变形,机床刚度,耐磨性等,我们仅在这里从机床精度对机床加工精度影响方面来研究提高机床加工精度的措施,其基本思想为：不影响、少影响、用补偿方法来消除,例如：为了加工出高圆度的工件在外圆磨床上,使死顶尖加工外圆,以使主轴回转误差不反映到工件上。再如：在精密螺纹磨床上,为提高工件螺纹的螺距误差,在机床传动链内增加误差补偿环节,以提高了工件的螺距精度。     

主轴回转运动精度的评定误差

利用刚体平面运动的瞬心理论,把主轴的回转运动作为刚体的平面运动来研究,定义了主轴的回转轴心误差的概念。提出了用回转中心误差定义的车削类主轴回转运动精度的评定误差和用回转轴心误差定义的镗削类主轴回转运动精度的评定误差的概念。并解决了加工精度与主轴回转运动精度的定量关系。     

数控机床热误差的建模与预补偿

研究了数控机床热误差的预补偿方法。建立了基于主轴转速的热误差自回归模型,从而不需要测量机床的温度场就可以预测热误差。在加工前通过修改工作的数控加工程度即可进行补偿,大大简化了误差补偿过程。可应用于中等精度的数控机床。     

数控机床主轴径向误差测量的相关研究

主轴回转误差是衡量机床性能的重要指标,是影响加工精度的主要因素。本文主要讨论了机床主轴误差的概念及各种径向回转误差测量的方法、误差分离原理及其实现。     

回转误差精确测试与特征评定值

推导并建立了在主轴回转误差测试中不受标准球偏心影响的表征主轴回转精度的数学模型。经推导和仿真处理，得出当主轴回转误差分解为不同频次的径向圆周运动或单向谐振动时，机床加工的各种形状、尺寸、形位误差的精确特征评定值。     

精密主轴回转误差标准测试的偏心分析

文章通过对精密主轴回转误差测试过程中偏心的研究,分析出造成偏心的影响因素以及造成偏心情况的作用原理,这样有助于提出针对性的措施减少精密主轴偏心情况的出现。     

精密主轴回转误差标准测试的偏心分析

文章通过对精密主轴回转误差测试过程中偏心的研究,分析出造成偏心的影响因素以及造成偏心情况的作用原理,这样有助于提出针对性的措施减少精密主轴偏心情况的出现。     

机床主轴回转误差补偿信号的测量理论和方法

本文提出了一种机床主轴回转误差补偿信号的实时测量方法。该方法采用两组差动电容传感器，直接相对主轴表面进行测量，传感器的极板设计成弧形以消除被测截面的形状误差分量。文中分析了测量方法的原理，介绍了简单、实用的测试系统。     

Study on the thermally inducedspindle angular errors of a five-axis CNC machine tool

Thermally induced spindle angular errors of a machine tool are important factors that affect the machining accuracy of parts. It is critical to develop models with good generalization abilities to control these angular thermal errors. However, the current studies mainly focus on the modeling of linear thermal errors, and an angular thermal error model applicable to different working conditions has rarely been investigated. Furthermore, the formation mechanism of the angular thermal error remains to be studied. In this study, an analytical modeling method was proposed by analyzing the formation and propagation chain of the spindle angular thermal errors of a five-axis computer numerical control (CNC) machine tool. The effects of the machine tool structure and position were considered in the modeling process. The angular thermal error equations were obtained by analyzing the spatial thermoelastic deformation states. An analytical model of the spindle angular thermal error was established based on the geometric relation between thermal deformations. The model parameters were identified using the trust region least squares method. The results showed that the proposed analytical model exhibited good generalization ability in predicting spindle pitch angular thermal errors under different working conditions with variable spindle rotational speeds, spindle positions, and environmental temperatures in different seasons. The average mean absolute error (MAE), root mean square error (RMSE) and R² in twelve different experiments were 4.7 μrad, 5.6 μrad and 0.95, respectively. This study provides an effective method for revealing the formation mechanism and controlling the spindle angular thermal errors of a CNC machine tool. The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00409-x     

基于CSO-SVM的数控机床主轴热误差建模

针对数控机床多热源所致的温升与主轴热误差之间复杂的非线性关系问题,提出一种鸡群优化（chicken swarm optimization, CSO）算法与支持向量机（support vector machines, SVM）相结合的主轴热误差预测模型（以下简称热误差模型）。以某精密数控机床的主轴单元为研究对象,采用五点法对其在空转状态下的轴向热变形进行测量,并借助热电偶传感器对机床的4个关键温度测点的温度进行采集。以SVM为理论基础,随机选取75%的数据样本进行训练,进而构建主轴热误差模型。其中,利用CSO算法优化SVM模型的惩罚参数c和核参数g,以提升热误差模型的预测能力及鲁棒性。以余下的25%的样本作为测试数据集,对所得热误差模型进行验证。利用CSO-SVM模型对不同工况下主轴的热误差进行预测,并将预测结果与测量结果进行对比。结果表明：当主轴转速为3 000 r/min时,CSO-SVM模型的平均预测精度高达97.32%,相较于多元线性回归模型和基于粒子群优化的SVM模型分别提升了6.53%和4.68%;当主轴转速为2 000, 4 000 r/min时,CSO-SVM模型的平均预测精度分别为92.53%、91.82%,表明该模型具有较高的预测能力和良好的鲁棒性。CSO-SVM模型具有较强的实用性和工程应用价值。     

机床主轴回转精度的CCD测量系统

利用数字图像处理技术，建立了一套主轴回转精度的CCD测量系统。该系统由CCD光电检测系统、微机和数据处理软件组成。软件系统基于Matlab开发，硬件部分由高速CCD摄影机等组成。对数据处理和误差评定进行了探讨。该系统用于车床主轴回转精度的实际测量，取得了良好效果。     

主轴回转误差Prony谱模型

本文提出了建立轴回转误差连续模型的Ｐｒｏｎｙ谱分析方法，并通过奇异值分解来确定误差信号中所含的谐波个数，为从根本上解决圆度与圆柱度误差分离技术中的“谐波抑制”问题奠定了基础。     

基于最近点配准的精密零件线轮廓度误差评定

提出一种基于最近点配准的评定方法。利用黄金分割法寻找实测点所对应的理论曲线上的最近点,再用奇异值分解求实测点与最近点配准中需要的旋转矩阵和平移矩阵,从而实现实测点与最近点的配准,在此基础上对线轮廓度误差进行计算。实验结果表明该方法可以高效地对精密零件的线轮廓度误差进行评定。     

A new active error compensatory method of machinery accuracy†

A new active error compensatory method for on-line cutting control has been developed for reduction of the form errors in machining. This approach is a combination of in-process gauging and model active compensatory control. In this approach, there are two features of substantial importance: stochastic modelling and optimum forecasting. Through stochastic modelling, the cutting tool error motions can be represented by a simple model without the necessity of obtaining the complex cause-and-effect relationships between various errors and error sources, and more importantly, it is possible to account for both repeatable and non-repeatable parts of errors. Optimum forecasting is an important prerequisite for a rational control strategy, considering the inevitable time delay associate with sensing, computation and actuation. The proposed control method was implemented for the control of cylindricity in boring operations. Through the controller simulation based on experimental measurements, the improvement in cylindricity accuracy confirms the effectiveness of this proposed strategy     

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen (DO) is an important indicator of aquaculture, and its accurate forecasting can effectively improve the quality of aquatic products. In this paper, a new DO hybrid forecasting model is proposed that includes three stages: multi-factor analysis, adaptive decomposition, and an optimization-based ensemble. First, considering the complex factors affecting DO, the grey relational (GR) degree method is used to screen out the environmental factors most closely related to DO. The consideration of multiple factors makes model fusion more effective. Second, the series of DO, water temperature, salinity, and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform (EWT) method. Then, five benchmark models are utilized to forecast the sub-series of EWT decomposition. The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm (PSOGSA). Finally, a multi-factor ensemble model for DO is obtained by weighted allocation. The performance of the proposed model is verified by time-series data collected by the pacific islands ocean observing system (PacIOOS) from the WQB04 station at Hilo. The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), mean absolute percent error (MAPE), standard deviation of error (SDE), and coefficient of determination (R²). Example analysis demonstrates that: ① The proposed model can obtain excellent DO forecasting results; ② the proposed model is superior to other comparison models; and ③ the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.     

Optimization of process variables in drilling of carbon fiber reinforced plastics using various multi criteria decision making approaches

Carbon fiber-reinforced polymers are one of the lightweight materials used in structural design due to their exceptional mechanical performances. The drilling operation is indispensable as it facilitates the assembling of various manufactured components. However, drilling of fibrous laminates is deemed difficult in comparison to the traditional metals because of the anisotropic and non-homogeneous nature. The present work addresses the parametric effect on the drilled hole delamination and further reduces it with an optimal combination of parameters for multi-objectives using different multi-criterion decision-making techniques. Initially, the response surface-based regression model of delamination as a function of three static inputs has been developed, further revised with induced thrust as well as mean torque for the improvisation of the prediction capability. Finally, for the overall improvement, a decision-making model has been used that includes grey relation analysis, technique for order performance by similarity to ideal solution, and VIšekriterijumsko Kompromisno Rangiranje method. The delamination was found to be minimum at a low drill point angle (100°), high spindle rotation (2150 min⁻¹ ), and low feed rate (0.025 mm/rev) due to reduced thrust force. The mean absolute prediction error was significantly improved considering root mean square torque rather than axial thrust with process variables.