基于马尔可夫随机场工件表面纹理模型的刀具状态监测

基于马尔可夫随机场理论，建立了工件表面纹理图像的马尔可夫随机场纹理模型，并对工件表面纹理图像的特点进行了分析。在实验数据的基础上，对工件表面纹理图像的特征参数进行提取，提出采用相对距离作为刀具磨损程度的评价指标。指出三阶马尔可夫随机场能比较充分地反映工件表面纹理图像的特征。实验结果表明，基于马尔可夫随机场的工件表面纹理分析方法能够较好地适用于刀具状态监测。     

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。     

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.     

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。     

面向遥感图像水域分割的图像熵主动轮廓模型

为提高遥感图像水域分割的准确度,结合高分率遥感图像中水域与背景纹理复杂度差异较大的特点,将图像熵引入到CV模型中,提出两种图像熵主动轮廓模型用于高分辨率遥感图像的水域分割。其中,针对水域纹理相对简单的遥感图像,在CV模型中引入零水平集内的图像熵而构成局部图像熵主动轮廓模型,可以有效降低背景中灰度值与水域近似的区域发生误分,从而提高水域分割的准确度;针对水域纹理相对复杂的遥感图像,在CV模型中同时引入零水平集内外图像熵而构成全局图像熵主动轮廓模型,改进了水平集函数进化过程中对灰度信息的依赖,并能使零水平集进化到全局最优,进一步提高了遥感图像中水域分割的准确度。针对高分辨率遥感图像中的湖泊、河流和海域分割对比实验结果表明:局部图像熵主动轮廓模型的分割精确率分别为90.1%、81.5%和93.6%,F值分别为0.94、0.885和0.96;全局图像熵主动轮廓模型的分割精确率分别为94.5%、85.3%、94.9%,F值分别为0.956、0.895、0.967。本文提出的两种图像熵主动轮廓模型均能有效减小背景误分,提高了遥感图像水域分割的准确度。     

CONDITION MONITORING FOR INDEXABLE CARBIDE END MILL USING ACCELERATION DATA

In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for indexable tungsten carbide end milling tools is presented based on the Fourier transform and statistical analysis of the vibrations of the tool during the machining operations. Using a low-cost, tri-axial piezoelectric accelerometer, the presented algorithm demonstrates the ability to accurately monitor the condition of the tools as the wear increases during linear milling operations. One benefit of using accelerometer signals to monitor the cutting process is that the sensor does not limit the machine's capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique, four tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool's wear and is shown to be sensitive enough to provide sufficient time to replace the insert prior to damage of the machine tool, cutter, and/or workpiece.     

基于机器视觉的刀具状态监测研究进展

介绍了基于机器视觉的刀具磨损监测的三种方法:基于刀具表面图像的视觉监测方法、基于工件表面纹理图像的视觉监测方法和基于切屑图像的视觉监测方法。综述了各种方法的研究进展,比较了各自的优缺点,分析了当前的研究热点和发展趋势。     

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。     

Monitoring of hard turning using acoustic emission signal

Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve.     

基于谐波特征和GA-SVM的刀具状态监测

刀具状态的监测是实现机械加工自动化重要的一环.为了有效地捕捉刀具的状态信息,提出了一种基于谐波特征和GA-SVM(遗传-支持向量机)相结合的刀具状态监测方法.该方法运用小波变换提取AE信号的谐波特征信息,作为支持向量机的输入参数,GA寻找SVM建立刀具状态模型的最优参数,通过训练建立模型.结果表明,该方法能有效监测刀具磨损状态.     

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.     

钻头磨损检测与剩余寿命评估

对钻头的磨损程度进行实时检测有助于对钻削加工过程实施预防性维护,提醒及时换刀。针对自动化生产中的刀具监测问题,给出一个基于主轴电流检测的钻头磨损状态分析和剩余寿命预测的应用策略。通过主轴电流传感器采样加工过程的电流信号,使用一个滑动窗口从连续采样数据中得到真实加工段数据,采用小波包分解的方法进行特征提取。基于Fisher标准筛选出最能表达磨损过程的若干特征。最后利用逻辑回归法和自回归滑动平均模型相结合的方法评估当前钻削加工的可靠性,预测钻头的剩余寿命。试验证明此方法的有效性,可为换刀决策提供依据。     

Surface textural analysis using acousto optic emission- and vision-based 3D surface topography��a base for online tool condition monitoring in face turning

In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. Thus, by monitoring the machined surface topography of the workpiece and extracting the relevant information the cutting process and tool wear state should be able to be monitored and quantified. But the effects of vibrations have been paid less attention. The work in the present paper is divided into two parts. First part consists of a data acquisition and signal processing using acousto optic emission sensor (i.e., laser Doppler vibrometer) for online tool condition monitoring and the second part of the work presents the surface topography analysis of machined surfaces during the progression of the tool wear. Most of the work presented is also a study where surface metrology is being used to measure all aspects of the machining in combination with an online metrology tool. The encouraging results of the work pave the way for the development of a real-time, low cost, and reliable tool?Ccondition?Cmonitoring system. A high degree of correlation is established between the results of the acousto optic emission signal- and vision-based surface textural analysis in identification of tool wear state.     

Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Tool condition monitoring has found its importance to meet the requirement of production quality in industries. Machined surface texture is directly affected by the extent of tool wear. Hence, by analyzing the machined surface images, the information about the cutting tool condition can be obtained. This paper presents a novel technique for tool wear classification using hidden Markov model (HMM) technique applied on the features extracted from the gray level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull and dull tool states. The proposed method is found to be cost effective and reliable for on-machine tool classification of cutting tool wear with an average of 95% accuracy.     

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%。     

Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision

Although literature on the measurement of flank wear and crater wear in single-point turning tools using machine vision is well documented, the study on the effect of nose radius wear on the roughness profile and dimensional changes of workpiece is less explored. The measurement of flank wear using the 2-D profile of the tool nose region or the roughness profile of the workpiece has also not been attempted in the past. In this work, the nose radius wear of cutting tools and roughness profile of turned parts in a lathe operation were measured using the machine vision method. The flank wear width VB_C in the nose area was determined from the nose radius wear using the tool setup and machining geometry. The nose radius wear was also determined from the roughness profile of the workpiece and used in calculating the flank wear width. Comparison between the maximum flank wear width VB_Cmax determined from the roughness profile and that obtained using a toolmaker’s microscope showed a mean deviation of 5.5%. This result indicates that flank wear can be determined fairly accurately from the workpiece roughness profile if the tool and machining geometry are known.     

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.     

An automatic system based on vibratory analysis for cutting tool wear monitoring

The aim of this work is to develop a new, simple to use and reliable automatic method for detection and monitoring wear on the cutting tool. To achieve this purpose, the vibratory signatures produced during a turning process were measured by using a three-axis accelerometer. Then, the mean power analysis was proposed to extract an indicator parameter from the vibratory responses, to be able to describe the state of the cutting tool over its lifespan. Finally, an automatic detector was proposed to evaluate and monitor tool wear in real time. This detector is efficient, simple to operate in an industrial environment and does not require any protracted computing time.     

TRIBOLOGY ISSUES IN MACHINING

Machining is the process of shaping materials into useful articles by removing the unwanted material. In traditional machining processes such as in cutting and grinding, the unwanted material is removed by mechanical means using a cutting tool. Since the tool makes contact with the workpiece and either the tool, the workpiece, or both are in motion, tribology (i.e., the study of rubbing or sliding) becomes an important issue. Tribology has a crucial and significant role as an enabling technology, since tool wear is a major problem in the production of reliable and cost-effective products. This paper reviews recently published articles related to the wear of cutting tools and grinding wheels. These papers are classified into such areas as the wear process, measurement of wear, reduction of wear through the use of different cutting tools and abrasive materials, strategies used to monitor wear during machining and grinding, and in-process control of machining to compensate for tool wear.