基于加工表面盒维数的刀具磨损状态研究

在车削加工过程中,随着刀具磨损量的增加,在工件表面的纹理结构发生变化,依据工件纹理的变化能够间接判断刀具的磨损程度。将分形理论引入到基于图像的刀具状态监测领域,研究二维离散图像信号盒维数的具体实现算法以及盒维数与刀具磨损量之间的变化关系。实验表明:随着刀具磨损量的增加,盒维数具有缓慢上升的趋势,利用这一特征可有效实现刀具磨损状态的监测。     

基于均值漂移与自适应阈值分割的刀具磨损检测方法

针对车刀表面粗糙度大、磨损区域图像难以准确分割的问题,提出一种基于均值漂移和自适应阈值分割的刀具磨损检测方法.这一方法通过频率域滤波过滤图像中无效的高频噪声信息,对降噪图像进行形态学处理.利用均值漂移聚类算法进行预分割处理,简化纹理信息,并突出磨损区域特征.通过自适应阈值分割算法准确提取刀具磨损区域,同时计算刀具的磨损宽度均值.试验表明,采用所提出的刀具磨损检测方法,分割准确率达到97.96％,能够有效判别刀具的磨损状态.     

刀具磨损的在线检测/实时补偿方法

刀具磨损的在线检测/实时补偿技术是现代制造系统的关键性技术之一。本文分析了刀具状态在线检测的方法,深入研究了一种在线检测/实时补偿刀具磨损的方法——工件尺寸间接法。利用在线检测仪器测得被加工工件尺寸,和预先设定的工件尺寸进行比较后将差值反馈到CNC系统,CNC系统根据反馈结果对机床进给做出相应的调整,达到补偿刀具磨损的目的。     

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WPD_EMD和SVM刀具磨损故障诊断模型

通过采集2种磨损程度不同的同类型刀具加工工件时机床主轴的振动信号,提出WPD_EMD和SVM故障诊断模型判断刀具磨损程度。首先利用小波包工具去除高频噪声信号,其次利用EMD分解得到若干个固有模态函数和一个残差,计算各个固有模态函数和EMD分解前信号的相关系数,合并相关系数大的固有模态函数得到新信号。计算新信号的绝对均值作为时域特征参数。选取若干组试验数据作为支持向量机训练集,建立判断刀具磨损程度大小的故障诊断模型。试验表明该故障模型预测刀具磨损程度准确率100%,为判断刀具实时加工工件的磨损程度提供新的途径。     

基于Elman神经网络的刀具磨损状态识别技术

由于刀具磨损声发射信号的能量分布与刀具磨损状态密切相关,可以利用谐波小波包方法提取刀具磨损声发射信号的特征能量,对各频段能量做归一化处理,与切削三要素组成特征向量输入到Elman神经网络,通过神经网络判别刀具磨损状态。实验结果表明,刀具磨损产生的声发射信号频率主要集中在10Hz~130k Hz之间,将谐波小波包和Elman神经网络结合的方法可以有效地识别刀具磨损状态。     

频带能量特征法在声发射刀具磨损监测系统中的应用

基于对声发射(AE)信号特点的分析和小波包分解理论对不平稳信号特征提取的优势,提出一种利用AE信号的能量变化来监测刀具磨损状态的方法。该方法利用db8小波基对AE信号进行5层小波包分解,将分解后各频带上的能量值作为特征参数,并组成特征向量。分别提取在新刀和刀具磨损状态下的特征向量,根据其变化即可判别刀具磨损的程度。试验结果验证了该方法在刀具磨损判析中的可用性。     

整数小波提升分解的刀具磨损检测方法

针对刀具磨损的特点,提出了一种新的刀具磨损检测方法.首先,利用提升格式,确定小波整数化单层提升分解的方法;其次,利用对小波单层分解的近似子图进行零均值化处理以消除光照的影响;然后,对零均值化的近似子图、水平细节及垂直细节子图进行标准化处理;在此基础上,对分解各子图进行选择性的图像融合处理;最后通过oust方差法进行分割从而实现对刀具磨损的检测.实验表明,所采用的方法能够有效抑制图像背景干扰,能够有效地实现刀具磨损检测.     

基于纹理特征的刀具状态监测技术

针对切削加工中刀具磨损会对加工精度产生不良影响的问题,基于磨损程度不同的刀具切削工件时将产生不同的表面纹理,采用灰度行程长度法对工件表面图像的纹理特征进行了分析.试验结果表明,以长行程加重参数作为评价刀具磨损程度的特征参数可以取得较好的效果,并且其物理意义明确.     

基于列相关分析的工件表面纹理描述

文中在提出纹理直线度概念的基础上,采用三列均值法对工件表面纹理图像进行旋转角度的校正,选取直线度最理想的直线ones(M,1)作为标准列,用纹理图像的每一列对其进行相关分析,实验结果表明:采用列相关分析法可使工件表面纹理的特征得到合理的描述,并能反映出工件表面纹理的直线度随刀具磨损的变化趋势,在刀具的急剧磨损阶段具有明显的波谷转折,因此,该方法能应用于刀具磨损状态的识别,是一种很有参考价值的方法。     

基于工件纹理的刀具磨损研究

应用方向测度法分析了工件表面显微纹理图像的特性,工件表面的纹理测度值随着切削加工的持续进行总体变化趋势是逐渐变小的。与实际工件表面纹理的粗糙度越来越大、方向性也逐渐变得混乱相比较,两者具有明显的一致性。实验结果表明方向测度法能够很好地反映车削加工纹理及其刀具磨损的内在关系。     

基于图像处理的刀具角度测量系统

针对加工过程中刀具磨损会影响零件加工精度的问题,利用图像处理技术实现了刀具角度的测量,以达到对刀具磨损情况进行动态跟踪的目的。以Matlab软件作为图像处理平台,通过图像灰度转换、二值化、边缘提取等技术手段,获得了刀具边缘轮廓,进一步用最小二乘法将轮廓拟合,最终求出了刀具角度。实验结果表明,该方法能较好地测量刀具角度,也可用于刀具几何参数的测试。     

Optimal design of electrode cooling system for resistance spot welding with the response surface method

In the last decade, the progress of surface metrology has led to improved 3D characterisation of surfaces, offering the possibility of monitoring manufacturing operations and providing highly detailed information regarding the machine tool condition. This paper presents a case study where areal surface characterisation is used to monitor tool wear in peripheral milling. Due to the fact that tool wear has a direct effect on the machined workpiece surface, the machined surface topography contains much information concerning the machining conditions, including the tool wear state. By analysing the often subtle changes in the surface topography, one can highlight the tool wear state. This paper utilises areal surface characterization, areal auto-correlation function (AACF) and pattern analysis to illustrate the effect of tool wear on the workpiece surface. The result shows the following: (1) tool wear, previously difficult to detect, will influence almost all of the areal surface parameters; (2) the pattern features of AACF spectrum can reflect the subtle surface texture variation with increasing tool wear. The authors consider that, combined analysis of the surface roughness and its AACF spectrum are a good choice for monitoring the tool wear state especially with the latest developments in on-machine surface metrology.     

An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates.     

Tool Chatter Monitoring in Turning Operations Using Wavelet Analysis of Ultrasound Waves

This paper presents a new method for tool chatter monitoring using the wavelet analysis of ultrasound waves. Ultrasound waves are pulsed through the cutting tool towards the nose and are reflected back off the cutting edge. Fluctuating states of contact and non-contact between the tool insert and the workpiece, which are generated as a result of tool chatter, affect the amount of the transmitted ultrasound energy into the workpiece material and, in turn, the amount of the reflected energy. The change in the energy of the echo signals can be related directly to the severity and frequency of tool chatter. Wavelet packet analysis was used to filter the ultrasound signals. A three-layer multilayer perceptron (MLP) artificial neural network (ANN) was used to correlate the response of the ultrasound sensor to the accelerometer measurement of tool chatter. The main advantage of the ultrasound sensor is its ability to monitor other parameters such as the first contact of the tool and workpiece tool chipping and flank gradual tool wear. Experimental results show that the severity of tool chatter can be successfully monitored using the proposed ultrasound system. The system response to various frequency levels of tool chatter was investigated, however, the measurement of the chatter frequency is beyond the system capability at the current time.     

The monitoring of flank wear on the CBN tool in the hard turning process

In precision hard turning, tool flank wear is one of the major factors contributing to the geometric error and thermal damage in a machined workpiece. Tool wear not only directly reduces the part geometry accuracy but also increases the cutting forces drastically. The change in cutting forces causes instability in the tool motion, and in turn, more inaccuracy. There are demands for reliably monitoring the progress of tool wear during a machining process to provide information for both correction of geometric errors and to guarantee the surface integrity of the workpiece. A new method for tool wear monitoring in precision hard turning is presented in this paper. The flank wear of a CBN tool is monitored by feature parameters extracted from the measured passive force, by the use of a force dynamometer. The feature parameters include the passive force level, the frequency energy and the accumulated cutting time. An ANN model was used to integrate these feature parameters in order to obtain more reliable and robust flank wear monitoring. Finally, the results from validation tests indicate that the developed monitoring system is robust and consistent for tool wear monitoring in precision hard turning.     

硬脆及黑色金属材料的单点金刚石车削加工技术综述

单点金刚石车削技术是产生纳米特征表面的光学元件重要制造工艺之一。此加工技术在空间科学、生物医学工程、军事、国防和光学等领域有着广泛的应用。然而,金刚石刀具在切削硬脆和黑色金属材料时受到限制,如刀具磨损加剧、刀具寿命缩短以及工件表面加工质量降低等。为了减少刀具磨损和提高工件表面加工质量,相关学者提出了不同的解决方案,将从单点金刚石车削辅助工艺、工件改性、刀具性能改善和超硬材料及刀具方面梳理面向提高硬脆和黑色金属材料加工质量的单点金刚石车削加工技术相关研究,分析当前各种加工技术的优势与局限,提出未来将多种能场辅助的单点金刚石车削技术和基于聚焦离子束改性的金刚石刀具技术作为研究的重点。     

基于细胞神经网络图像识别的刀具监控方法的研究

针对传统的刀具磨损状态监测方法与磨损程度无严格对应关系的缺点,提出一种新方法——采用普通的CCD摄像机拍摄刀尖形状的图像,经细胞神经网络图像处理后与正常的图像进行比较,判断刀具是否产生磨损。该系统可用于实现自动化精密加工过程中的实时在线工件形状监控和刀具诊断,仿真证明了理论算法的有效性。     

Influence of cooling condition of tools on central deformation of workpiece and tool wear in cross wedge rolling

In this paper, a thermal–mechanical coupled simulation model for two-roll cross wedge rolling (CWR) was developed to investigate the influence of cooling condition of tools on central deformation of workpiece and tool wear by using three-dimensional rigid-plastic finite element method. Based on the simulation results, the information about central deformation of workpiece and tool wear with and without tool cooling was compared and analyzed. The study results indicate that forging quality and tool life can be improved by means of cooling the tools with cooling water. Subsequently, an industrial example of CWR in blank forming for engine connecting rod was presented to verify the feasibility of study results. In this industrial application, higher forging quality and longer tool life were obtained, which benefits the decrease of production cost. This study provides insights into the mechanisms of central deformation of workpiece and tool wear under different cooling conditions of tools in CWR process as well.