数控机床刀具磨损监测实验数据处理方法研究

数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.有针对性地回顾了国内外各种分析刀具磨损信号方法的研究工作,详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术的实现形式.通过比较各种数据处理方法的优缺点,提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来发展的主要方向.     

多传感器融合下多工况刀具磨损状态预测的深度森林方法研究

准确监测加工过程刀具磨损状态有助于避免因刀具失效导致的产品质量问题。 建立不同工况的刀具磨损监测模型,往 往需要对每组工况调参以保证精度。 为减少调参并保证预测精度,结合深度森林的超参数少、参数对模型不敏感和训练过程自 适应等优点,利用深度森林建立了多传感器信号及多工况下自主特征选择的刀具磨损状态预测模型。 基于 3 组不同工艺参数 下 TC18 铣削过程的多传感器及磨损数据,以及预测与健康管理(PHM)学会 2010 年高速数控机床刀具健康预测竞赛的开放数 据,深度森林在 3 组工况的预测精度分别为 95. 35% 、96. 63% 和 97. 06% ,在 PHM 数据上为 98. 95% ,验证了深度森林对多工况 下刀具磨损预测的高精度和适用性,为在线监测技术提供了有力的指导。     

数控机床刀具磨损与破损的声发射监测法

数控切削加工过程中刀具的磨损与破损是数控机床常见的故障之一,而刀具的磨损与破损程度直接影响零件的加工质量.所以对刀具状态的实时监测就显得十分关键,阐述了用声发射法对其进行在线监测的方法.     

基于神经网络多传感器融合的刀具摩损定量监测的研究

研究了前馈神经网络（ＦＮＮ）的自构造型学习算法，提出了基于神经网络多传感器融合的一般结构及刀具磨损监测方法，讨论了多传感器的选择、多传感器信号的采集与预处理以及多传感器信号的特征选择与正规化处理，并就铣削过程的刀具磨损监测进行了实验研究，结果表明，所提出的方法可获得９３％的识别率。     

[设备健康管理]基于机床信息的加工过程刀具磨损状态在线监测

为实现刀具磨损状态的在线监测,提高监测系统的实用性,提出一种基于机床信息的加工过程刀具磨损状态在线监测方法。采用OPC UA通信技术在线采集与存储数控机床信息,得到与磨损相关的机床内部过程信息,并基于这类信息与相应的刀具磨损信息,利用卷积神经网络建立了刀具磨损状态识别模型。应用案例证明了该方法的监测性能,与其他传统监测方法相比,该方法更适用于实际的生产加工。     

基于机床信息的加工过程刀具磨损状态在线监测

为实现刀具磨损状态的在线监测,提高监测系统的实用性,提出一种基于机床信息的加工过程刀具磨损状态在线监测方法。采用OPC UA通信技术在线采集与存储数控机床信息,得到与磨损相关的机床内部过程信息,并基于这类信息与相应的刀具磨损信息,利用卷积神经网络建立了刀具磨损状态识别模型。应用案例证明了该方法的监测性能,与其他传统监测方法相比,该方法更适用于实际的生产加工。     

基于主轴电流信号多特征融合的刀具磨损状态监测

为实现在正常生产条件下进行刀具磨损的长期在线监测,提出了基于主轴电流信号和粒子群优化支持向量机模型(PSO-SVM)的刀具磨损状态间接监测方法.首先对数控机床主轴电机电流信号进行分析,将与刀具磨损相关的主轴电流信号多个特征参数和EMD能量熵进行特征融合作为输入特征向量;其次,通过粒子群寻优算法(PSO)对支持向量机模型...     

基于长短记忆网络的刀具磨损监测方法研究

刀具磨损监测是推动数控机床稳定运行的有效手段。为实现刀具健康实时监测,设计一种基于长短记忆网络的刀具磨损监测方法。将实时采集的切削力信号进行去噪、降维等预处理;用堆叠自编码器进行特征提取,获得影响刀具磨损的本质特征;用长短记忆网络构建刀具磨损监测模型,实现加工过程中刀具磨损监测。利用铣削实验数据进行实例验证,获得磨损量平均绝对误差为0.060 80的铣刀磨损量监测模型,验证了该方法的有效性。     

基于深度学习的刀具磨损监测研究现状

基于传统机器学习的刀具磨损监测模型监测精度低且计算复杂度高,难以满足智能制造的发展需求。而基于深度学习的刀具磨损监测模型数据处理和特征提取能力较强,可明显提高监测精度,使加工过程更智能化,因此广泛应用于刀具磨损监测。根据采用的模型,将基于深度学习的刀具磨损监测分为基于卷积神经网络的刀具磨损监测、基于稀疏自编码网络的刀具磨损监测、基于深度置信网络的刀具磨损监测、基于长短时记忆网络的刀具磨损监测和基于混合模型的刀具磨损监测,介绍了各种深度学习模型的基础理论及基本结构,总结了国内外基于深度学习模型的刀具磨损监测方法,分析了存在的问题,并指出了未来的发展方向。     

基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别

刀具磨损监测对于提高加工过程的精度和自动化程度具有重要意义。本文提出一种基于RBF函数神经网络的刀具磨损状态监测模式。该系统利用声发射传感器对切削过程进行监测,采用多分辨率小波分解技术从声发射信号中提取反映刀具磨损的特征向量,并输入RBF神经网络,实现了刀具磨损的自动识别。     

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.     

基于振动法的刀具磨损状态监测研究

数控机床加工过程中,刀具是直接与金属材料接触并参与切削过程的工作部件。在金属切削加工过程中刀具不可避免地产生磨损现象,提高刀具使用寿命,降低因为刀具磨损而产生的损失,对刀具的工作状态实施监测意义十分重大。基于振动测试法的刀具磨损状态监测主要以采集到的振动信号作为依据,对振动信号作时域、频域和时频分析来提取有效的特征量,结合工件被加工表面的粗糙度情况以及刀刃的磨损状况,判断出在加工过程中刀具的实时磨损规律,实现刀具磨损程度的有效控制。     

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.     

一个应用于自动化加工系统的综合刀具监测系统

对于自动化加工系统、刀具破损和异常磨损的有效实时监测是一个亟待解决的问题。本文用声发射信号监测加工中心上各种刀具的破损、折损,针对多种工序、多咱切削条件的复杂情况,进行了可变参数的模式识别算法的研究。基于这个算法,开发了一个综合刀具破损监测系统。这个系统针对自动化加工基本单元——加工中心的车、镗、铣多种工序,使得自动化加工系统的综合监测成为可能。实验验证表明,识别成功率大于90%。     

基于随机森林与主成分分析的刀具磨损评估

刀具是数控机床的重要零部件,其性能直接影响着生产加工精度。为实现机床刀具磨损程度的在线分级评估,提出一种刀具磨损状态的评估方法,该方法结合随机森林与主成分分析模型,建立不同工况下主轴电机电流传感器信号样本与刀具磨损等级的非线性映射关系。在不同加工条件下进行刀具性能试验,采集主轴电机的电流信号和铣削加工参数。对信号利用小波包分解、时域统计、频域分析提取特征,利用随机森林得到刀具磨损的分级评估结果。该方法可有效解决样本不平衡的问题,与常见的组合分类方法AdaBoost所得结果相比,该模型能准确地反映刀具的磨损程度,鲁棒性更好。该方法仅利用数控机床内置传感器实现,无需改动机床结构,不影响主轴动态加工性能,可广泛应用于工业数控机床刀具的磨损评估。     

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.     

Assessment and visualisation of machine tool wear using computer vision

Tool wear monitoring is an integral part of modern CNC machine control. Cutting tools must be periodically checked for possible or actual premature failures, and it is necessary to record the cutting history for a tool’s full life of utilisation. This means that an on-line monitoring system would be of great benefit to overall process control in manufacturing systems. Computer vision has already shown promise as a candidate technology for this task. In this paper, we describe the use of digital image processing techniques in the analysis of images of worn cutting tools in order to assess their degree of wear and thus remaining useful life. It is shown that a processing strategy using a variety of image texture measures allows for effective visualisation and assessment of tool wear, and indicates good correlation with the expected wear characteristics.     

Competitive parameter optimization of multi-quality CNC turning

Surface roughness, tool wear, and material removal rate (MRR) are major intentions in the modern computer numerical controlled (CNC) machining industry. In this paper, the ${\text{L}}_9 \left( {3^4 } \right)$ orthogonal array of a Taguchi experiment is selected for four parameters (cutting depth, feed rate, speed, and tool nose runoff) with three levels (low, medium, and high) in optimizing the finish turning parameters on an ECOCA-3807 CNC lathe. The surface roughness (R_a) and tool wear ratio (mm^?2) are primarily observed as independent objectives for developing two combinations of optimum single-objective cutting parameters. Additionally, the levels of competitive orthogonal array are then proposed between the two parameter sets. Therefore, the optimum competitive multi-quality cutting parameters can then be achieved. Through the machining results of the CNC lathe, it is shown that both tool wear ratio and MRR from our optimum competitive parameters are greatly advanced with a minor decrease in the surface roughness in comparison to those of benchmark parameters. This paper not only proposes a competitive optimization approach using orthogonal array, but also contributes a satisfactory technique for multiple CNC turning objectives with profound insight.     

谈数控机床刀具的监控技术

数控机床刀具中的检测与监控就是采用检测与监控系统通过传感和分析处理相关信息,从而对数控机床运行状态和加工过程进行检测与控制,保证数控机床的加工质量,防止产生废品,减少废品率,提高机械加工生产率,提高加工过程的安全性,优化配置制造资源。而检测与监控系统的成败主要取决于系统所收集的信号即有关监控参数的正确选择,也取决于其物理结构上的传感器和监控器以及硬件和软件系统。