以机器视觉为基础的刀具磨耗侦测系统研究

刀具的几何轮廓与工件加工精度有着密不可分的关系,若刀具因磨耗产生形状误差,会影响产品精度或造成产品品质不良,故需对其进行精确监控,以确保产品品质与精度。本文提出以机器视觉为基础的非接触式刀具磨耗侦测系统,针对常用的车床刀具加以探讨,运用数位影像处理方法,以视觉辅助系统测量刀具磨耗,解决了传统人工检测的安全性不足,避免了因刀具磨耗产生的形状误差而影响产品的精度与品质。     

Real‐Time Monitoring of Tool Fracture in Turning Using Sensor Fusion

The sensor fusion method using both an acoustic emission (AE) sensor and a built-in force sensor is introduced for on-line tool condition monitoring during turning. The cutting force was measured by a built-in piezoelectric force sensor, which was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A significant drop in cutting force indicated tool breakage. The algorithm was implemented in a DSP board and the monitoring system was installed on a CNC lathe in an FMS line for in-process tool-breakage detection. The proposed system showed an excellent monitoring capability.     

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.     

移动式焦炉火道的自动测温装置

焦炉火道温度的测量是炼焦生产中一个十分重要而又十分棘手的问题。本文提出一种安装于装煤车上的焦炉火道自动测温装置,采用非接触式测温探头,并利用无线发射/接收装置,以及微处理机控制,实现焦炉加热过程火道温度的自动测量。     

Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring     

Knife-edge interferometry for cutting tool wear monitoring

This paper presents a novel technique for more easily measuring cutting tool wear using knife-edge interferometry (KEI). Unlike an amplitude splitting interferometry, such as Michelson interferometry, the proposed KEI utilizes interference of a transmitted wave and a diffracted wave at the cutting tool edge. In this study, a laser beam was incident on the cutting tool edge, and the photodetector was used to determine the interference fringes by scanning a cutting tool edge along the cutting direction. The relationship between the cutting tool wear and interferometric fringes generated by edge diffraction phenomena was established by using the cross-correlation of KEI fringes of two different cutting tool-edge conditions. The cutting tool wear produced the phase shift (attrition wear) and the decay of oscillation (abrasive wear) in the interferometric fringe. The wear characteristics of the cutting tool with a radius of curvature of 6 mm were investigated by measuring the interferometric fringes of the tool while cutting an aluminum work piece in a lathe. As a result, the attrition and abrasive wear of cutting tool showed a linear relationship of 5.62 lag/wear (μm) and 1.14E-3/wear (μm), respectively. This measurement technique can be used for directly inspecting the cutting tool wear in on-machine process at low-cost.     

A non-contact method for detecting on-line industrial robot position errors using a microwave Doppler radar motion detector

To be useful, industrial robots must meet positioning accuracy requirements for their given applications. Off-line calibration generally improves robot positioning accuracy to levels needed for open-loop use in most industrial applications. Applications that require greater accuracy with respect to external assemblies generally turn to closed-loop control or passive compliance. However, industrial robot systems do not generally monitor in-process robot position to detect machine faults that can lead to product faults, scrap, machine damage, and additional costs. To achieve greater operational efficiencies, new non-invasive, non-contact methods for monitoring robot position are needed. The investigators developed a low-cost method for in-process industrial robot position monitoring using a Doppler motion detector and a statistical position error measure. The method detects position errors at robot repeatability levels.     

利用光散射理论在线检测表面粗糙度

提出以光散射理论为基础,利用光学技术实现表面粗糙度的在线测量。该测量系统可对范围在0.02~0.16内的表面粗糙度实现非接触无损检测。     

车刀磨损的特点

通过对大量因磨损而失效的车刀刀头的分析,给出了车刀各磨损参数与切削时间的关系曲线,并探讨了前、后刀面磨损的钝化判据。在生产中应用钝化判据,可及时磨刀或换刀,从而减少车刀损耗、延长车刀使用期,提高劳动生产率和产品加工质量。     

基于数控纵切自动车床上镍基高温合金的加工

随着航空航天工业的发展,对于高温合金工件的需求日益增长。针对镍基高温合金现场加工难、切削性能差和刀具磨损快等问题,本文在数控纵切自动车床上采用切削试验的方法,对镍基高温合金GH2132在不同切削工况下的刀具磨损以及零件表面质量进行了对比分析,明确了适用的刀具和合理的切削用量及切削速度。分析认为,性能优异的涂层刀具在选择了合适的进给量之后,完全能够满足镍基高温合金的切削加工,相对于其他硬质合金刀具,无论在生产效率还是产品质量方面,都有了很大的提升,并且节约了生产成本。     

In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product. Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods. Most research into tool wear monitoring using vision systems focusses on off-line measurement of wear. The effect of wear on surface roughness of the workpiece is also studied by measuring the roughness off-line using mechanical stylus methods. In this work, a vision system using a CCD camera and backlight was developed to measure the surface roughness of the turned part without removing it from the machine in-between cutting processes, i.e. in-cycle. An algorithm developed in previous work was used to automatically correct tool misalignment using the images and measure the nose wear area. The surface roughness of turned parts measured using the machine vision system was verified using the mechanical stylus method. The nose wear was measured for different feed rates and its effect on the surface roughness of the turned part was studied. The results showed that surface roughness initially decreased as the machining time of the tool increased due to increasing nose wear and then increased when notch wear occurred.     

基于Archard模型的机床导轨磨损模型及有限元分析

机床导轨的磨损是导致机床精度降低或丧失的重要因素。针对机床导轨磨损深度的预测问题,提出了一种基于Archard模型与有限元模拟试验的机床导轨磨损分析方法。该方法基于修正的Archard模型形成一种磨损深度的计算公式,并采用一种离散化计算方法进行求解;结合ANSYS软件,提出机床导轨磨损有限元分析模拟的方法及流程;最后,以车床滑动导轨为例,用有限元试验方法对机床导轨的磨损过程进行模拟分析,得到磨损深度关于磨损次数的计算公式,其结果表明:机床导轨磨损深度的仿真模拟值与实际值具有一致性。     

介绍一种车床刀具磨损视觉检测装置

对于现代机床而言,刀具的磨损状态监测显得日益重要。在此设计并制作出了一种新颖的在机视觉检测装置,不仅可以实现视觉系统与机床的结合,而且具有良好的隔振性能。实验结果表明,采用这种机构,可以使摄像机拍摄到比较清晰的图像,为实现车刀磨损在机检测提供了条件。     

Measurement of nose radius wear in turning tools from a single 2D image using machine vision

Flank and crater wear in cutting tools have been extensively studied in the past using direct and indirect methods. In finish and hard turning, however, nose radius wear plays a greater role in determining the surface quality of the finished product. This is due to the direct interaction between the tool nose area and the workpiece during machining. Nose radius wear can be measured from the 2D profiles of the tool before and after machining using a vision method. This approach, however, requires two images of cutting tools to be recorded and precisely aligned before subtraction. This work proposes a new method of measuring nose wear area from a single 2D image of the worn tool. The nose profile is extracted in the Cartesian coordinates and converted to a polar-radius plot. From the plot, the nose wear area is determined by simple subtraction.     

非球回转曲面工件非接触在线测量新方法

以坐标测量法为基础，选择涡流式非接触传感器，并通过误差补偿技术，达到三维非球回转曲面在线精密测量的目的。根据所提测量原理，在普通数控车床上，对模拟试件进行了在线测量并取得较好结果。     

3D measurement of crater wear by phase shifting method

In this paper, a new method of non-contact tool wear measurement is presented. It employs a phase shifting method using fringe patterns to measure crater wear by constructing a 3D map of the tool insert. Four fringe patterns with various phase-shifts are projected onto the rake face of the tool upon which four gray-level images are captured. By solving and then unwrapping the phase map with the four images, a 3D profile of the tool is obtained to provide the overall size of the crater wear land. Sample tool inserts with various crater wear surfaces have been evaluated with the method and the results demonstrate that the system is robust to ambient light and is accurate and applicable for use in real industrial environment to measure crater wear.     

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.     

振弦式陀螺频率检测结构设计与振动分析

针对微陀螺高精度和微型化的发展趋势,依据振弦式陀螺的工作原理建立了振弦式陀螺频率检测数学模型;应用ANSYS软件分析了陀螺检测结构参数影响,优化了振弦式陀螺整体结构并建立了振动响应仿真分析。结果表明振弦式陀螺驱动结构振动响应随着驱动力的增大,驱动响应位移量也随之增大,且驱动力与位移量之间具有很好的线性关系,可以很好地控制振弦式陀螺框架驱动力,优化振弦式陀螺结构尺寸,从而验证了振弦式陀螺电磁驱动与频率检测方案模型的可行性。     

基于长短时记忆卷积神经网络的刀具磨损在线监测模型

为了提高机械加工过程中刀具磨损在线监测的准确性,提出了一种基于长短时记忆卷积神经网络（LSTM-CNN）的刀具磨损在线监测模型。在该监测模型中,通过振动、力、声发射传感器对刀具切削过程中的振动、力和声发射信号进行采集,采集的数据其本质为时间序列数据。考虑采集数据的序列和多维度特性,采用LSTM-CNN网络对采集的数据进行序列和多维度特征提取,利用线性回归实现特征到刀具磨损值的映射。通过实验验证了该模型的有效性和可行性,模型的精度较其他几种方法有了较大的提高。     

管道机器人无线电磁自适应定位技术

针对金属管道机器人跟踪定位问题,提出了一种改进的自适应无线电磁定位方法,在未知管道几何尺寸和周围环境电磁参数的情况下实现了管道内机器人的三维定位.首先,通过磁偶极子模型分析了管道外极低频电磁场分布规律.其次,建立了六传感器接收天线的自适应定位模型,设计了电磁发射和接收电路,基于该模型推导出了含有6个未知数的非线性方程组,并分别采用3种算法对其进行了仿真分析.最后,分别对不同壁厚及不同埋藏深度管道进行了试验研究.试验结果表明,在管道壁厚为5.74 mm,埋藏深度为0.8～2 m的情况下,总位置误差的平均值小于18.7 cm,即比原模型减少了3.4 cm,满足机器人实际工作中的定位需要.