铣削508Ⅲ钢硬质合金刀具敏感性分析

508Ⅲ钢材料应用于核岛AP1000蒸发器水室封头中,是一种高强度、高硬度和高断面收缩率的低碳合金钢。硬质合金刀具在切削508Ⅲ钢时,会产生较大的切削力以及切削振动,从而影响刀具使用寿命。本文进行硬质合金刀具铣削508Ⅲ钢试验,探究切削力以及切削振动信号对刀具磨损敏感性的变化趋势的影响,并运用互相关函数分析切削力以及切削振动信号对硬质合金刀具磨损形态的敏感程度。试验结果表明:切削力较切削振动相比,对刀具磨损形态的影响较大,并在切削速度为298m/min时,切削力、切削振动对刀具磨损形态互相关程度较高。为进一步研究通过切削力等信号检测刀具磨损状态提供试验及理论参考。     

刀具磨损监测与车削仿真研究

金属加工过程中,切削刀具的状态对于生产效率和表面加工质量有重要影响,因此刀具磨损监测具有重要意义。刀具磨损监测是柔性制造系统研究工程的一个重要课题。切削力信号作为加工过程中最稳定和最可靠的信号,和刀具磨损密切相关。从实验上分析切削力与刀具磨损的相关性,提出刀具切削力变化与磨损变化是一致的。基于有限元分析软件对车削加工进行仿真研究,模拟了切削力的大小分布,并将模拟结果与实验结果进行了比较分析,为实际工艺参数的选择提供了理论指导。     

基于小波变换的刀具磨损检测方法

提出采用切削力信号的奇异性指数作为衡量刀具磨损的参量。利用小波变换对切削力信号进行分析 ,变换结果的模极大值点反映了刀具发生磨损或破损的时刻 ,而其奇异性指数的大小则反映了刀具的磨损状况。试验结果表明了该方法的有效性。     

刀具磨损过程中切削力动态分量信号分形维数的变化规律研究

研究了刀具磨损过程中切削力动态分量信号分形维数的变化规律 ,发现分形维数在刀具初期磨损和剧烈磨损阶段较正常磨损阶段高。由于切削力信号动态分量的分形维数随刀具磨损状态的变化而变化 ,使得它可以作为切削过程中判断刀具工作状态的一个参数     

基于MIC和改进Bagging-GPR的刀具磨损预测

为实现刀具磨损的准确预测，对加工过程的换刀和参数优化提供指导，提出一种基于最大信息系数(MIC)和改进的Bagging集成高斯过程回归(Bagging-GPR)的刀具磨损预测方法，建立切削力信号与刀具磨损间的非线性映射关系。采集加工的切削力信号，运用时域、小波包分解和经验模态分解提取切削力信号特征，并利用MIC分析特征与刀具磨损的相关度来实现特征选择，避免预测模型的“维数灾难”。为提高预测模型的精度，考虑高斯子模型内部核函数的差异性及准确性，利用Bagging对高斯核函数进行随机组合，作为各子模型的核函数，构建改进的Bagging-GPR模型实现刀具磨损值预测，并基于铣削实验数据验证了所提方法的有效性和优异性。     

基于刀具磨损和切屑形成对切削Ti6Al4V的切削力特性研究

为了研究钛合金Ti6Al4V切削过程中的切削力特性,采用硬质合金涂层和无涂层刀具进行了外圆干车削试验,提取切削力信号,通过研究切削力的静动态特性,揭示了切削力与切削速度、刀具材料、刀具磨损以及切屑形成的关系.结果表明:钛合金切削过程中,切削力的静态分量中径向力Fp最大,直接导致刀具后刀面磨损;随着切削速度的变化,切削力的变化是由刀具磨损、材料本身的特性等多方面因素综合作用的结果,切削力动态分量分形维数可用于刀具状态监控;锯齿形切屑的产生与切削力的高频变化有直接的关系,锯齿生成频率可以作为切削力动态分量频率的一个表征,选取适当的切削参数可以降低由于锯齿屑产生引起的切削力振动.     

基于力信号的低频振动制孔刀具磨损状态分析

为分析碳纤维增强树脂基复合材料(CFRP)/钛合金(TC4)叠层材料低频振动制孔工艺下刀具磨损状态,开展基于切削力信号的制孔刀具磨损状态研究.通过采集CFRP/TC4叠层材料低频振动制孔过程中的切削力信号,进行时域和频域分析,探讨各信号特征量与刀具磨损状态之间的联系.研究结果表明:CFRP/TC4叠层材料低频振动制孔轴...     

基于小波分析的切削力信号奇异性检测

利用小波变换模的极大值和信号奇异点的关系,分析了用Lip指数来描述的切削力信号局部奇异性.通过观察奇异点的位置等信息得到切削刀具的磨损情况.通过对实际刀具磨损的在线监测数据分析,证明了采用小波变换检测刀具磨损这一方法的有效性.     

车刀磨损破损在线监控技术的研究

分析研究了用切削力信号监控车刀磨损与破损的可行性,提出了用切削合力方向系数法、动态切削力的自相关系数法和摩擦颤振能量法三种在线检测刀具磨损和用切削合力方向系数监控破损的新方法。提出了刀具磨损与破损的监控方案,并研制了刀具磨损与破损的监控系统。     

基于模糊小波极限学习机的刀具磨损状态识别

刀具磨损作为机械加工过程中的常见现象,直接导致了切削力增加、工件表面粗糙度恶化以及尺寸超差等不良后果,极大地影响加工效率.采集加工过程中切削力、振动及声发射信号,利用线性回归法对信号进行特征提取及降维;采用不同刀具的磨损数据训练模糊小波极限学习机(FWELM),降低加工过程的不确定性对识别模型的影响,并解决加工系统的信息模糊造成的建模困难问题,提升刀具磨损识别模型的泛化能力.利用标准刀具磨损数据集测试结果证明,基于FWELM构建的刀具磨损状态识别模型识别的每个刀具磨损阶段的准确率及总体识别准确率皆高于极限学习机构建的识别模型.     

基于切削力信号时域频域特征融合的刀具磨损监测

从时域、频域提取了切削力信号特征参数随着刀具磨损量增加的变化规律,提取了切削力信号的峰值因子、Kurtosis系数和频段带能量作为刀具磨损量监测特征参数,并将各个特征量构成的特征矢量输入改进的多层反传神经网络进行融合,实现钻削过程刀具磨损量的智能识别。试验结果表明,该方法具有较高的识别精度和较强的抗干扰能力。     

基于神经网络的多特征融合刀具磨损量识别

采用切削力信号监测钻削过程钻头的磨损量 ,分别从时域、频域提取了切削力信号的均值、方差、峭度系数和特定频段能量作为刀具磨损的特征信号 ,讨论了特征信号随着刀具磨损量增加的变化规律 ,并将各个特征信号构成的特征矢量输入多层反传神经网络进行融合 ,实现钻削过程刀具磨损量的智能识别。试验结果表明该方法能有效实现多特征融合 ,但识别精度和推广能力有待进一步提高     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

Tool wear monitoring in drilling using force signals

Utilization of force signals to achieve on-line drill wear monitoring is presented in this paper. A series of experiments were conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals and to establish the relationship between force signals and tool wear as well as other cutting parameters when drilling copper alloy. These experiments involve four independent variables; spindle rotational speed ranging from 600 to 2400 rev min⁻¹, feed rate ranging from 60 to 200 mm min⁻¹, drill diameter ranging from 5 to 10 mm, and average flank wear ranging from 0.1 to 0.9 mm. A statistical analysis provided good correlation between average thrust and drill flank wear. The relationship between cutting force signals and cutting parameters as well as tool wear is then established. The relationship can then be used for on-line drill flank wear monitoring. Feasibility studies show that the use of force signal for on-line drill flank wear monitoring is feasible.     

Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys

Online monitoring and in-process control improves machining quality and efficiency in the drive towards intelligent machining. It is particularly significant in machining difficult-to-machine materials like super alloys. This paper attempts to develop a tool wear observer model for flank wear monitoring in machining nickel-based alloys. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by measuring the cutting force variations. The correlation between the cutting force components and the flank wear width has been established through experimental studies. It was used in an observer model, which uses control theory to reconstruct the flank wear development from the cutting force signal obtained through online measurements. The monitoring method can be implemented as an outer feedback control loop in an adaptive machining system.     

基于大数据模糊 PID 控制的切削机械加工稳定性分析

针对切削机械加工稳定性分析未考虑切削力信号和切削机械磨损之间的关系,导致切削机械加工稳定性较差的问题,提出一种基于大数据模糊 PID 控制的切削机械加工稳定性分析方法,对加工过程建模,同时通过历史切削机械加工数据组建切削力信号和磨损之间的映射关系。分析切削机械的加工性能,在构建数学模型的基础上,设计一种大数据模糊 PID 控制方法对切削机械加工稳定性实时控制,通过模糊控制规格对控制器参数实时调整,最终有效控制切削机械加工稳定性。仿真实验结果表明,在切削机械加工过程中,该方法可以有效控制切削机械加工稳定性。     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation

A tool wear monitoring system is indispensable for better machining productivity, with the guarantee of machining safety by informing of the time due for changing a tool in automated and unmanned CNC machining. Different from monitoring methods using other signals, the monitoring of the spindle current has been used without requiring additional sensors on the machine tools. For reliable tool wear monitoring, only the current signal from tool wear should be extracted from the other parameters to avoid exhaustive analyses on signals in which all of the parameters are fused together. In this paper, the influences of force components from different parameters on the measured spindle current are investigated, and a hybrid approach to cutting force regulation is employed for tool wear signal extraction from the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects, varied to regulate the force component from tool wear.