基于声发射信号的精密外圆切入磨削时间评估算法及试验研究

针对精密外圆切入磨削智能监控的需求,设计一种基于声发射信号的磨削时间在线评估方法。通过建立声发射信号方均根值曲线预测模型,获得声发射信号与磨削系统时间常数的关系,设计磨削系统时间常数在线计算方法;利用在线检测的声发射信号识别砂轮运动去除状态,推导基于声发射信号的外圆切入磨削表面粗糙度评价和工件几何精度预测模型,以此建立砂轮进给与驻留时间的评估算法;编写磨削时间分析评估软件,设计磨削时间在线评估方法,通过加工试验分析磨削时间对磨削加工精度与表面粗糙度的影响规律,并对评估算法进行验证。试验结果表明:该评估方法能够根据磨削时间有效评价加工质量,为精密外圆切入磨削智能监控与工艺优化提供决策依据。     

基于声发射信号的精密外圆切入磨削效率优化

针对精密外圆切入磨削加工时磨削效率问题进行研究。建立了磨削进给量与磨削时间的关系方程,进而得到磨削深度与时间的关系方程;建立了声发射信号的磨削理论模型,在线识别砂轮运动去除状态,通过加工试验分析磨削时间对磨削表面粗糙度和加工精度的影响;结合试验得到的声发射均方根信号进行曲线拟合,编写磨削时间在线分析软件,减少无效的磨削时间,提高磨削效率。试验结果表明:该方法能够在满足加工工艺的前提下,提高精密磨削切入磨的效率。     

基于时间常数外圆切入磨削砂轮钝化的监测方法

基于外圆切入磨削力模型研究,提出了一种在线监测功率信号的时间常数算法,并利用时间常数对外圆切入磨削砂轮钝化状态进行识别。为提高计算准确性,选取了进给阶段稳定功率信号和驻留阶段功率信号变化率对时间常数进行计算。通过不同磨削工艺参数和不同修整工艺参数实验,验证了利用功率信号的时间常数方法对砂轮钝化监测的有效性。     

基于磨削力模型的外圆切入磨削声发射信号研究

磨削过程中磨削力与声发射信号均方根值有较强的对应关系,对工件表面加工质量有很大影响。为便于研究外圆切入磨削质量和效率,在基于外圆切入磨削力的数学模型研究基础上,建立了声发射信号AERMS与径向进给速度、砂轮线速度及工件线速度的关系模型。通过磨削实验研究结果,验证了上述方法的有效性和实用性。     

采用遗传算法优化神经网络的铸铁表面粗糙度声发射预测

表面粗糙度是汽车发动机曲轴精密磨削加工中的一个非常重要的指标,在线监测表面粗糙度是曲轴智能磨削成功的标志。应用美国声学物理公司PAC的PCI-2声发射实验仪器测量磨削声发射信号,采用遗传算法优化BP神经网络,以磨削声发射信号均方根和快速傅里叶变换峰值为特征值,对平面磨削曲轴球墨铸铁材料QT700-2表面粗糙度成功进行了预测。与表面粗糙度的实测结果表明相对误差可控制在6.22%以下。     

工程陶瓷磨削声发射和磨削温度磨削力联合监测的研究

磨削声发射可以对磨削过程实时监测,磨削声发射信号均方根AERMS可以用来分析磨削过程的特征.为了使用磨削力、磨削温度、磨削声发射来联合对工程陶瓷氧化锆与氧化铝磨削过程进行全面监测和对工程陶瓷磨削机理进行深入研究,通过实验研究了磨削声发射信号与磨削力和磨削温度之间的内在联系,建立了工程陶瓷氧化锆与氧化铝磨削声发射信号与磨...     

外圆磨削在线优化

基于基本的磨削模型,利用小生境蚁群算法和小生境粒子群算法,对磨削进给量和磨削深度进行在线优化。并采用Matlab编程,利用声发射信号和优化模型,对磨削过程进行仿真,实现了外圆磨削实际磨削过程的在线监测和优化。对提高磨削质量和磨削效率,实现磨削过程的在线监测和智能化控制有着极大的推动作用。     

磨削外圆与内孔不同心的套类工件

我们知道内孔与外圆同心的套类零件,只要配作 一个芯轴即可实现外圆磨削,而内孔与外圆不同心的套类零件(如图1所示)的外圆磨削却有一定困难。如果我们只采用一个偏心芯轴来实现外圆磨削,由于芯轴     

切入式外圆磨削接触刚度与固有频率研究

基于外圆切入磨削系统动力学简化模型,对磨削材料去除率模型的时间常数进行研究,提出了切入式外圆磨削接触刚度的有效测量方法,并建立了磨削接触刚度与系统固有频率的关系模型。通过大量磨削实验,验证了不同磨削工艺参数与接触刚度、系统固有频率及颤振频率的变化规律。研究结果为后续避免或抑制磨削颤振以及磨削工艺参数优化研究提供了实验基础和理论依据。     

基于磨削热变形分析的切入式外圆磨削去除率的修正模型与实验研究

为解决切入式外圆磨削加工中磨削热变形引起的传统磨削去除率模型与实际磨削偏差较大的问题，基于磨削热变形机理分析，对传统切入式外圆磨削去除率模型进行了修正。通过分析切入式外圆磨削过程中砂轮与工件的磨削热变形量及热变形速率，确定了修正函数，建立了基于磨削热变形的切入式外圆磨削材料去除率修正模型，并通过磨削实验对模型进行了验证。结果表明，修正模型具有更高的准确性。     

Process monitoring in precision cylindrical traverse grinding of slender bar using acoustic emission technology

The precision cylindrical traverse grinding process of slender bar is very complex for the strongly time dependent properties of the wheel. Therefore, it is very difficult for operators to properly judge the grinding state using naked eyes and ears. This calls for automatic monitoring technology that can monitor the process in precision cylindrical traverse grinding to guarantee machining quality and productivity as well as reduction in cost. This study developed an automatic monitoring system for precision cylindrical traverse grinding of slender bar using Acoustic emission (AE) technology. Grinding tests on molybdenum were conducted under traverse conditions in a conventional cylindrical grinder. It was found that larger radial material removal depth results in larger root mean square value of Acoustic emission signals (AE_RMS). Based on this, the AE_RMS was analyzed and used to determine the finishing of spark-out process and the pre-processing of tool alignment. The variation tendency of AE_RMS in one spark-out process was applied to determine when a wheel wears out and has to be dressed. The experimental results showed that the AE system was effective to monitor the pre-processing of tool alignment, spark-out and wheel wear in precision cylindrical traverse grinding of slender bar.

     

Analysis of acoustic emission in precision and high-efficiency grinding technology

Current demands of machining hard and brittle materials at very small tolerances have predicated the need for precision and high-efficiency grinding. In situ monitoring systems based on acoustic emission (AE) provide a new way to control the surface damage and integrality of the components. However, a high degree of confidence and reliability in characterizing the manufacturing process is required for AE to be utilized as a monitoring tool. The authors established AE based online monitoring system and studied technique parameters versus the waveforms of AE under different working conditions. The results show that there are obvious mapping relations between the technique parameters of grinding and the effective values of the AE signals. Grinding along different directions would result in different strength of AE signal. Comparing with grinding along first longitude, fewer AE signal is released when grinding along latitude and better surface quality is generated. Similar variation tendency is observed no matter between AE root mean square (RMS) and linear speed or between surface roughness and linear speed which justify some kind of correlation may exist between AE RMS and surface roughness. The distance between the AE transducer and the AE source should be less than 80 mm while monitoring the process of grinding composite ceramics.     

磨削砂轮状态在线监测方法研究

提出了用声发射（AE）信号在线监测砂轮状态的方法.利用该方法可以监测工件材料、加工要求和磨削参数经常变化环境下的砂轮钝化程度和破碎情况;并采用神经网络建立了传感器信号与砂轮状态之间的非线性关系.     

轴颈外圆磨削成圆过程的双转子耦合模型及仿真算法

主轴轴颈外圆的圆度对机床主轴部件的回转精度具有直接而重要的影响。磨削加工是实现轴颈外圆高精度加工的最重要的手段。虽然磨削技术在主轴外圆加工上已长期应用,但是利用现有的研究成果,还难以对一个既定的磨削系统的磨削成圆过渡过程进行完整有效的定量仿真,也难以对磨削工件的最终圆度进行科学合理的预测。在吸收国内外磨削转子系统模型成果的基础上,进一步考虑外圆磨削过程中砂轮和工件之间的相互耦合作用、材料去除、转子振动及变形协调规律,建立了外圆磨削系统的双转子耦合振动模型,提出了模拟轴颈材料去除和圆度变化的"磨削力-瞬时磨削深度"迭代算法。利用所建立的双转子模型和迭代算法,对不同磨削策略下轴颈外圆成圆的过渡过程进行了定量仿真,再现了轴颈材料去除和外圆轮廓变化的全过程;比较研究了不同磨削策略对磨削效率和最终加工圆度的影响规律。进行了相应的磨削试验和对比验证,试验结果证实了所提出的轴颈外圆磨削的双转子模型和迭代算法的有效性和合理性。     

切入磨削与纵向磨削的磨削力分析与比较

研究了同时包含切入磨削和纵向磨削的复杂外圆磨削过程。根据纵向磨削过程的特点,将砂轮等效成若干个小砂轮,在传统阶梯模型的基础上构建了砂轮磨损的抛物线模型。推导了基于两种模型的纵向磨削切向分力和切入磨削切向分力的比较公式,两切向分力的比值反映了切入磨削和纵向磨削转换时切向分力的变化情况,它主要与磨削系数、砂轮宽度和纵向进给速度有关。采用砂轮主轴功率信号分析磨削切向分力,通过实验验证了抛物线模型更符合实际情况的结论。研究结果为采用磨削力信号和功率信号研究复杂磨削过程的监控提供了参考依据。     

Reduction of Air-Grinding Time Using Collaboration of Dual Sensors

Air-grinding time is inevitable in the grinding process. Since the low feedrate during air grinding makes the process inefficient, air-grinding time should be reduced. This paper presents a technology for reducing air-grinding time in cylindrical plunge grinding operations. The main idea is to estimate a distance, NAP (nearest approaching point), between the workpiece surface and the position of the grinding wheel. After detecting the NAP using an ultrasonic sensor, a CNC controller adjusts the feedrate using the feedrate override function with the help of the AE signal that gives the CP (contact point) information. The experimental results show that an ultrasonic and an AE sensor are good enough for detecting the NAP and the CP, respectively, and the system reduces the conventional air-grinding time by two-thirds.     

Acoustic emission as an indicator of material-removal regime in glass micro-machining

Acoustic emission (AE) spectra were recorded during microgrinding of brittle materials. It was found that the specific AE energy (i.e., the measured AE energy divided by the material removal rate) was lower for fracture-dominated grinding than for plastic flow-dominated grinding. Two subsequent experiments were performed to measure AE energy while holding the material-removal rate constant. By controlling either the critical depth of cut (for ductile-brittle transition) of the workpiece material, or the actual depth of cut of the grinding machine, the sensitivity of AE energy to grinding regime was investigated for grinding with a constant material-removal rate. Contrary to conventional thinking about the relative contributions of plastic flow and fracture in generation of AE activity, it was found that the AE energy was larger in ductile-regime grinding than in brittle-regime grinding, for identical material removal rates. As a result of the experiments described in this paper, it can be concluded that AE energy measured during microgrinding is sensitive to changes in the mechanism of material removal. For a given volume of material removed, there is more AE energy in a plastic flow-dominated process than in a fracture-dominated process. The relationship found between AE energy and material removal regime could lead to an in-process sensing strategy for controlling grinding ductility.     

磨削过程计算机集成智能监控系统

利用神经网络建立了磨削过程计算机集成智能监控系统,该系统将磨削过程监测、故障诊断和反馈控制组成一个有机整体.通过在线提取磨削声发射(AE)信号RMS峰值、FFT峰值、信号标准偏差以及信号累积幅值增量,可以实现磨削烧伤、磨削颤振、砂轮钝化等故障的在线实时诊断.通过反馈控制系统实现磨削参数的实时调整,提高了磨削加工的性能、效率和磨削质量的稳定性.实测结果证明了该系统的有效性.