球头铣刀刀具磨损建模与误差补偿

针对刀具磨损度量方式和模型建立的问题,以球头刀具为研究对象,提出球头铣刀刀具磨损的度量方式,建立球头刀具磨损模型.以复映磨损在硬度较软加工材料上的方式测量球头刀具磨损,确定刀具磨损模型系数,给出刀具磨损模型系数确定的具体实现方法.加工试验验证球头刀具磨损度量方式的合理性和所建立刀具磨损模型的正确性,同时针对数控铣削加工中球头铣刀刀具磨损引起的误差提出离线仿真误差补偿算法,给出离线仿真误差补偿算法的具体实现步骤,通过建立的刀具磨损引起的加工误差模型仿真获得加工走刀步的误差.对于误差超差的走刀步,预先修改数控加工(Numerical control,NC)程序,保证实际加工零件满足精度要求.误差补偿验证试验表明所提出的离线仿真误差补偿算法的正确性和有效性.     

虚拟制造中基于刀具磨损的复杂曲面加工误差补偿

铣削加工过程中刀具的磨损是产生曲面加工误差的重要原始误差,将刀具磨损引起的误差通过建立的误差模型进行定量补偿,是虚拟制造中的一项关键技术。研究了虚拟制造环境下基于球头铣刀磨损的曲面加工误差补偿,建立了与加工参数相关的球头铣刀磨损模型,用以衡量球头铣刀切削刃磨损量,提出球头铣刀铣削加工误差补偿方法,并经实验验证有效。     

虚拟制造中基于刀具磨损的复杂曲面加工误差补偿

基于数控铣削加工仿真系统,研究了在虚拟制造环境下对球头铣刀磨损引起的曲面加工误差的预测与补偿。建立了与加工参数相关的球头铣刀磨损模型,用于预测球头铣刀切削刃的磨损量,提出了球头铣刀铣削加工误差的补偿方法,并通过实验验证了该方法的有效性。     

考虑刀具磨损的数控加工误差的预测研究

将刀具磨损引起的误差通过建立的误差模型进行预测,是虚拟制造中的一项关键技术.通过分析影响刀具磨损的切削参数,针对硬表面的加工材料建立了基于相对切削时间的球头铣刀磨损模型,提出了虚拟制造环境下考虑球头铣刀磨损的复杂曲面加工误差预报方法.实验结果表明:该误差模型预报是有效的,并且为曲面加工误差预测、提高曲面加工精度和效率提供了理论依据.     

铣削加工中刀具磨损间接测量的研究

阐述了钛合金(TC4)的切削性能。通过TC4铣削加工时的刀具磨损试验,研究铣削钛合金时硬质合金刀具磨损情况。对TC4工件在不同的时间间隔内进行铣削试验,将每组试验加工后的刀具磨损情况在铝合金板加工形成一系列有刀具磨损信息的孔,利用三坐标测量机测量铝合金板上孔的信息。采用最小二乘方法处理测量的数据,从而得到刀具磨损的相关信息。     

刀具磨损的机器视觉监测研究

为提高铣削加工时的刀具利用率、降低刀具成本,提出采用机器视觉技术在机监测铣刀磨损状态,及时更换刀具。建立刀具磨损监测系统,由电荷耦合器件(Charge coupled device,CCD)相机获取刀具磨损图像,通过图像预处理、阈值分割、基于Canny算子和亚像素的边缘检测方法建立刀具磨损边界,提取刀具磨损量。开展GH4169镍基高温合金铣削实验,将监测系统检测的磨损量与超景深显微镜的测量结果进行比对,结果表明:该系统具有较高的检测精度,可实现铣削加工时刀具磨损状态的在机监测。     

虚拟制造系统中球头铣刀的磨损预测

基于球头铣刀铣削方式及铣削参数的特点,应用微分理论基本方法提出了一种虚拟制造系统中球头铣刀磨损预测的数学模型,为提高虚拟制造系统的真实感和实时性奠定了基础;该模型预测的结果与铣削试验的测量结果基本吻合。     

基于接触区域的球头刀五轴加工无颤振刀具姿态研究

为解决球头刀五轴铣削过程中接触区域获取困难和难以调整主轴转速实现稳定性铣削的问题,提出一种基于接触区域的姿态稳定性图获取方法。首先,建立了考虑刀具姿态与螺旋角影响的动态切削力模型。通过NX二次开发提取刀具与工件接触区域的初始数据,并采用一种坐标变换法获得不同刀具姿态下的接触区域。然后,使用全离散法构建姿态稳定性图判断了在不同刀具姿态下的铣削稳定性。最后,通过实验案例在五轴加工中心中进行球头刀铣削实验,验证了所提方法的有效性与可行性。现场加工表明,实验结果与预测值吻合良好。     

确定铣削力模型中刀具偏心参数的一种算法

对数控加工中球头铣刀铣削力建模时刀具偏心参数的确定进行了研究。在铣削力模型的建立中考虑了刀具偏心的影响,推导出刀具偏心参数的表达式,考虑到刀具单刃切削条件,提出了刀具偏心参数的确定算法。在通过铣削力试验数据计算铣削力系数的过程中,采用上述算法确定了刀具的偏心参数,用于铣削力的仿真预测中,仿真结果和铣削力试验的结果表明,算法效果良好。     

钛合金高速铣削刀具磨损机理和预测方法研究

钛合金的加工非常困难,刀具磨损严重。为了尽可能减小刀具磨损,在高速铣削状态下能预测刀具的耐用度,本文研究了在高速铣削钛合金时刀具磨损的机理,并提出了一种基于切削试验的刀具磨损预测方法,建立了粗、精加工两种状态下刀具磨损预测数学模型,研究了顺逆铣方式、刀尖圆弧半径对刀具磨损的影响。     

A new tool wear estimation method based on shape mapping in the milling process

In the milling process, tool wear has a great influence on product machining quality, especially for a difficult-to-cut material. In this paper, a new approach based on shape mapping is proposed to acquire tool wear in order to establish an off-line tool wear predicting model for assessing the degree of wear and remaining useful life. The new approach maps tool wear shape into a metal material by milling holes mode after finishing each of the machining experiments. The metal material has low influence on tool wear compared to the experimental material. Thus, a series of mapped holes, which can represent the worn tool information, are formed on the metal material when finishing all milling experiments. These mapped holes on the metal material are analyzed according to all types of milling cutters in order to establish the relationship between the characteristic parameters of these mapped holes and tool wear. According to the established relationship, the characteristic parameters of these mapped holes are measured on the coordinate measure machine. The tool wear of each machining experiment can be obtained from the measured characteristic parameters of these mapped holes. The new tool wear estimation method does not require the stoppage of the machine tool and the removal of the cutter to measure tool wear in the process of conducting tool wear experiments. The new method can increase the machine tool efficiency of tool wear machining experiments and provide an efficient way to acquire tool wear in the process of establishing an off-line tool wear predicting model. In order to verify the new tool wear estimation method, a series of machining experiments were conducted on the five-axis machining center for cemented carbide cutting tool milling stainless steel. Experiments show that the shape mapping strategy of tool wear can allow for an effective assessment of tool wear and indicate good correlation with the expected wear characteristics and easily conduct tool wear experiments.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

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     

异形螺杆铣削过程刀具磨损建模研究

介绍了一种螺杆铣削过程刀具磨损建模的方法。该方法针对螺杆加工中变切削参数的工况，提取了振动信号和功率信号的刀具磨损特征值，并建立了信号特征值与刀具磨损量之间的映射关系，从而得到刀具磨损模型。实验证明，由此建立的刀具磨损模型。能够排除切削参数变化的干扰，可以较好地反映加工中刀具磨损状态。同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

An online optical system for inspecting tool condition in milling of H13 tool steel and IN 718 alloy

Tool wear adversely affects surface integrity due to higher cutting forces and temperatures. However, an accurate and efficient tool wear measurement is a challenging problem. The traditional direct tool wear measurement methods such as optical microscope and scanning electron microscope (SEM) leads to error of tool reassembly, tool orientation, and low accuracy, while the indirect measurement methods cause poor accuracy. In this paper, tool wear phenomena in milling of tool steel AISI H13 and superalloy Inconel 718 have been studied. A novel online optical system has been developed to integrate with a CNC machine to directly inspect and measure tool wear conditions in milling which minimizes the above-mentioned measurement errors in traditional methods. The evolutions of tool flank wear of PVD-coated inserts in end milling of the two materials were inspected to demonstrate the function of the optical measurement system. The tool wear evolution versus cutting time were obtained and examined. The characteristic images of fast tool wear in milling of Inconel 718 were captured using SEM and compared with the optical images to estimate flank wear. Three basic modes of tool wear—flank wear, nose wear, and crater wear—were compared and analyzed. A two-parameter method has been developed to evaluate both flank wear and nose wear with respect to cutting time in milling of Inconel 718. The advantages of the on-line optical tool inspection system were discussed.     

数字孪生驱动的数控铣削刀具磨损在线监测方法

针对数控铣床不断老化导致刀具磨损预测模型误差较大,加工过程中动态数据难以在线采集等问题,提出一种数字孪生驱动的刀具磨损在线监测方法。采用神经网络对加工过程中的多源数据进行特征提取,建立考虑机床老化的刀具磨损时变偏差量化模型,并在此基础上提出数控铣削刀具磨损的在线预测方法;开发了面向刀具磨损的数控铣削数字孪生系统,在线感知加工过程中的动态数据并实时仿真刀具磨损过程;最后,将该方法应用于实际加工中并与其他的预测方法进行了对比,结果表明该方法有效降低了机床老化带来的误差,实现了刀具磨损的精确预测。     

曲面铣削过程加工路径对切削力和磨损的影响研究

针对不同走刀路径下的复杂曲面加工过程进行球头铣刀铣削Cr12MoV加工复杂曲面研究,分析不同走刀路径下铣削力和刀具磨损的变化趋势。试验结果表明:通过对比分析直线铣削和曲面铣削过程中的最大未变形切屑厚度,可以得出单周期内曲面铣削的力大于直线铣削过程的力,铣削相同铣削层时环形走刀测得的切削力普遍大于往复走刀测得的切削力;以最小刀具磨损为优化目标,运用方差分析法分析得出不同走刀路径的影响刀具磨损的主次因素,同时利用残差分析方法建立球头铣刀加工复杂曲面刀具磨损预测模型,并通过试验进行验证。