基于不等高度数据采集的刀具磨损三维检测

由于刀具磨损对切削过程以及表面加工质量有着重要的影响,因此刀具磨损程度的检测对于提高零件的表面加工质量有重要的意义。文章提供了一种新的刀具磨损检测方法,该方法主要基于在不同高度对磨损区域拍照所得的图像,并对磨损区域进行相应的三维重构,最终能得到刀具磨损区域的三维形貌图,其不仅能使得刀具的磨损区域得到重现,而且能对磨损的体积进行较为精确的测量。将该技术对实际加工中不同切削条件下的刀具的磨损进行检测,最终得到了不同切削条件下的刀具的磨损,结果表明该方法对于刀具磨损的检测不仅方便,而且精度较高。     

基于加工表面纹理连通区域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,表现为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.具体处理方法为:选取阈值作图像分割,对二值图像作区域标记,区域标记的最大值称作连通区域数,将二值图像的连通区域数作为刀具磨损的特征量.实验结果表明,随着刀具磨损量的增加,图像的连通区域数随之增加,它为刀具磨损在线检测提供了一种有效的手段.     

金刚石涂层刀具铣削氧化锆义齿性能研究

氧化锆陶瓷义齿不论从材料的切削加工性,还是从义齿的结构特点上,对刀具的切削性能及寿命都有极高的要求。本文先对立铣刀加工预烧结氧化锆义齿的特点进行分析,得出义齿在加工中的失效主要是由切削冲击、过切和欠切引起的,然后通过刀具的寿命试验和磨损试验对金刚石涂层刀具和TiAlN涂层刀具进行了寿命、磨损的对比分析。在试验分析的过程中通过超景深显微镜对后刀面磨损带宽度和铣削距离进行记录,通过扫描电镜等仪器分析了两种不同涂层材料刀具加工后的前、后刀面磨损形貌和刀具的磨损机理。最终得出在磨钝标准:VB为0.1 mm的情况下,金刚石涂层刀具的寿命约是TiAlN涂层刀具的6.5倍,在切削过程中发生的主要磨损为磨粒磨损、化学磨损和冲蚀磨损。     

基于机器视觉的铣刀侧铣磨损测量

针对硬质合金铣刀在侧铣工件过程中出现的刀具磨损问题,采用机器视觉的方法对磨损刀具进行磨损量检测.通过对铣刀底面ROI区域的提取以及刀刃部分直线的拟合,计算出刀具的旋转角度,从而实现对铣刀侧面磨损区域的定位.在此基础上又采用改进的图像形态学和灰度线性变换相结合的图像增强算法,能够很好地解决硬质合金铣刀磨损检测中出现的反光...     

钛合金铣削刀具的磨损对工件表面沿主轴方向粗糙度的影响

采用硬质合金立铣刀对Ti6Al4V进行高速铣削正交试验,将新刀具所加工的工件表面粗糙度和后刀面磨损至0.05 mm左右时的刀具所加工出来的工件表面粗糙度值进行对比分析,研究磨损后的刀具对工件表面粗糙度的影响。利用粗糙度仪对工件表面粗糙度进行测量,使用超景深显微镜对加工后的工件表面形貌以及刀具磨损情况进行观察,并利用测力仪测量铣削加工过程中刀具产生的铣削力。结果表明:当刀具后刀面磨损至0.05 mm时,其切削参数对工件表面的粗糙度影响大小与刀具崭新时的不一样,这是由于刀具的磨损导致在加工时刀具发生了振颤,从而影响到了工件沿机床主轴方向的粗糙度使其粗糙度增大。     

基于分数布朗运动的工件表面图像分析及其在刀具状态检测中的应用

介绍了刀具磨损状态的检测方法和分数布朗运动(FBM)的基本理论。应用分数布朗运动场模型对切削工件的表面图像进行纹理分析,提取纹理特征后,根据分形维数D和图像上对数功率谱的拟合曲线的平均斜率k来判断刀具的磨损状态。与实验得到的结果相比较,该方法的结果令人满意。     

刀具前角对聚晶金刚石刀具磨损过程的影响

为了研究刀具前角对聚晶金刚石刀具磨损过程的影响,采用两种不同前角的聚晶金刚石刀具进行各向同性热解石墨切削加工试验,对刀具磨损过程、磨损机理和表面加工质量进行了分析对比研究。通过对试验结果的研究分析表明:两种不同前角的聚晶金刚石刀具磨损都主要发生在后刀面,切削刃都出现了崩刃现象。刀具磨损区域都出现了平行沟槽式的磨损形貌,但是出现的时间存在明显差异。磨粒磨损是这两种不同前角的聚晶金刚石刀具的主要磨损机理,前角为5°的聚晶金刚石的磨损机理还包括冲蚀磨损。与前角为5°的聚晶金刚石刀具相比,前角为-20°的聚晶金刚石刀具具有较好切削加工性能。     

二硅酸锂玻璃陶瓷车削实验刀具磨损模型研究

使用硬质合金数控车刀对二硅酸锂玻璃陶瓷进行车削实验,观测刀具磨损形貌。结果表明:刀尖和后刀面是磨损较严重的部分,刀具磨损形式主要是磨料磨损和黏结磨损。采用刀具磨损体积作为刀具磨损情况的评价指标,并建立车削过程刀具体积磨损的理论模型,结果表明刀具磨损体积与刀具和工件的材料属性及刀具角度有关;随车削长度增加,刀具实际磨损体积增加,将实验结果与根据模型计算所得的理论值进行对比,二者基本吻合。     

PCD刀具高速干式切削铜合金的磨损研究

使用PCD刀具对锡青铜合金材料进行高速干式切削试验,分别采用扫描电镜（SEM）、X射线能谱仪（EDS）对刀具的磨损形貌进行观察和磨损区域化学成分进行分析,并以此研究了PCD刀具的磨损机理。结果表明:在高速干式切削条件下,PCD刀具主要表现为前刀面的片状剥落和后刀面的轻微破损;同时还伴随着机械应力和热应力冲击下的脆性破损,出现崩刃、切削刃整体断裂以及前后刀面的大面积剥落。刀具磨损的主要原因是高温作用下的氧化磨损和扩散磨损。      

钛合金深孔钻削刀具磨损试验分析

目的探索枪钻钻削Ti6Al4V钛合金刀具的磨损特性,探讨刀具磨损对钻削轴向力的影响。方法设计深孔钻削试验,每孔钻深575 mm,每钻削一个孔,使用共聚焦显微镜对刀具磨损特性及磨损值进行分析,并使用测力仪对轴向力信号进行提取。通过显微镜观测,对刀具的磨损形式进行分析,结合刀具实际磨损情况,给出刀具的磨损等级。通过对轴向力的分析,研究刀具磨损量对于钻削轴向力的影响。结果由刀具磨损曲线可知,在整个钻削试验过程中,磨损过程可分为三个阶段:初期磨损、正常磨损、剧烈磨损。外刃第一后刀面的平均磨损量及最大磨损量在磨损的三个阶段中始终大于前刀面。当钻削深度达到11 m以后,刀具整体磨损速率上升,进入剧烈磨损阶段;当钻削深度达到14 m以后,外刃第一后刀面最大磨损量急剧增加。轴向力变化曲线呈现初期磨损阶段基本保持不变,正常磨损阶段平稳增加,剧烈磨损阶段趋于稳定的变化趋势。结论刀具的主要磨损形式为前刀面和外刃第一后刀面的表面烧灼及粘结磨损,外刃和侧刃的破损及崩刃,导向面的大面积剥落继而形成凹坑,三种情况共同导致刀具失效。刀具剧烈磨损阶段,刀具磨损速率迅速增加,切削力较大,因此实际加工过程中应在剧烈磨损阶段之前对刀具进行重磨。     

基于三维图像处理的车刀磨损缺陷检测方法研究

针对目前车刀磨损检测中所获取的二维图像处理无法提取车刀磨损区域三维特征的问题，提出一种基于结构光投影技术的方法，提取车刀磨损区域三维特征信息。对获取的信息进行分割，分离车刀中的垫片点云和刀头部分点云；然后对垫片点云进行最小二乘拟合平面处理，将拟合的平面与刀头部分点云进行最大距离求解，并用标准量块进行实际物理距离换算；最终获得刀头的最大实际磨损量，为工人更换刀头提供数据参考。实验结果表明：在对车刀进行磨损区域量化处理中，该方法可以获取车刀刀头的具体磨损量值，且具有较高的精度和较低的经济成本，该系统的误差小于0.05 mm,可满足实际生产需求。     

Flank wear measurement by a threshold independent method with sub-pixel accuracy

This paper presents an image processing procedure to detect and measure the tool flank wear area. Unlike the traditional thresholding-based methods, a rough-to-fine strategy is considered in this paper whereby a binary image is first obtained and used to find the candidate wear bottom edge points; then a threshold-independent edge detection method based on moment invariance is employed for more robust determination of the wear edge with sub-pixel accuracy. To shorten computation time, a critical area is initially defined and the subsequent procedure is confined to processing this area as the region of interest. Images from three types of inserts, A30N, AC325 and ACZ350 under different cutting conditions are captured with the similar illumination conditions after milling. The measured results obtained with the proposed method from these images are compared with those obtained by direct manual measurement with a toolmaker's microscope and a method based totally on binary image contour detection. The proposed method is shown to be effective and suitable for the unmanned measurement of flank wear.     

Research on tool wear detection based on machine vision in end milling process

This paper suggests a novel technique for the tool wear measurement based on machine vision. Tool images are captured between cutting operations using a machine vision system. The gray value difference threshold is determined from the tool image itself and the reference line is found to locate the tool in the image. The edges of the tool wear region are extracted by column scanning. A method of continuity testing is used to find the correct edge position in each wear column. To achieve a more accurate result, the sub-pixel edge detection technology is adopted to extract the edges. Finaly, the tool wear parameters can be obtained after rebuilding the top edge of the wear region and determining the bottom edge of the wear region. The measurement results gotten by the proposed method are compared with those gotten by measuring directly with a microscope. The proposed scheme is shown to be reliable and effective for the automated tool wear measurement.     

An automated flank wear measurement of microdrills using machine vision

The objective of this study is to develop an automated flank wear measurement scheme using vision system for a microdrill. The images of worn-out microdrills were captured after the hole-drilling tests on a 10-layered printed circuit board (PCB). The models were evaluated and validated based on the acquired image with a computer-based acquisition system. Edge detection was employed to extract the boundary with a pair of edge points, including both raising and falling edges, to compute the height of the cutting plane. The flank wear area, average flank wear height, and maximum wear height are computed by using this approach to evaluate the tool life. Experimental results show that the proposed scheme is reliable and effective for the automated frank wear measurement of microdrill in PCB production.     

基于数字图像的刀具磨损状态检测技术

采用数字图像分割和亚像素边缘检测等图像处理技术自动测量刀具磨损边界的磨损量并与刀具磨钝标准比较,判断刀具的磨损状态,实现刀具磨损状态的间歇式在线自动检测。针对刀具的边缘特征,运用了中值滤波、对比度拉伸、迭代自适应二值化、基于分水岭分割、Roberts边缘算子、图像匹配、Zernike正交矩边缘检测和曲线拟合等数字图像处理技术,完成了刀具磨损量微米级的高精度检测。     

基于细胞神经网络刀具磨损图像的预处理

提出了一种基于细胞神经网络的刀具磨损图像处理方法，通过设计细胞神经网络参数，运用细胞神经网络对刀具的二值图像平滑滤波，边缘提取，通过仿真证明该方法是有效的，由于细胞神经网络易于用VLSI实现并且并行处理速度快，因此该方法对刀具的磨损状态机器视觉检测中的图像处理具有实用意义。     

3D tool wear measurement and visualisation using stereo imaging

Tool wear detection has traditionally restricted itself to 2D study and measurement. A new technique for the measurement and visualisation of tool wear pattern has been presented in this paper. This method provides visualisation of the tool wear geometry using a pair of stereo images and generates the volume of crater wear as a new parameter for inspection. The results demonstrate that the volume of crater wear can be effectively used to characterise the tool wear. The average depth of the tool wear and surface area of the crater are also obtained as parameters for measurement. The technique provides a fast and a possible on-line method of tool wear analysis and measurement.     

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

Application of statistical filtering for optical detection of tool wear

The application of automated tool condition monitoring systems is very important for unmanned machining systems. Tool wear monitoring is a key factor for optimization of the cutting processes. Basically, tool wear monitoring systems can be subdivided into two classes: direct and indirect. Currently direct tool wear monitoring systems are most frequently based on machine vision by camera. Several approaches have been studied for tool wear detection by means of tool images, and an innovative statistical filter proved to be very efficient for worn area detection. A new approach has been implemented and tested in order to develop an automatic system for tool wear measurement. This new approach is described in this paper and the main topics related to tool wear monitoring using wear images have been discussed.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.