Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis .     

Monitoring of tool fracture in milling

For untended manufacturing operations, in-process monitoring of tool fracture plays a critically important role. A tool fracture feature in the spectrum of the displacement signal of the spindle in milling has been discovered. Therefore, a new signal processing algorithm called the band-limited average energy method using the tool fracture feature to monitor tool fracture is proposed. The energy content of the tool fracture feature is extracted and normalised to detect tool fracture. It is shown, by theoretical studies and experimental results, that tool fracture can be detected under varying cutting conditions in milling.     

Novel tool wear monitoring method in ultra-precision raster milling using cutting chips

Tool wear monitoring is a popular research topic in the field of ultra-precision machining. However, there appears to have been no research on the monitoring of tool wear in ultra-precision raster milling (UPRM) by using cutting chips. In the present research, monitoring tool wear was firstly conducted in UPRM by using cutting chips. During the cutting process, the fracture wear of the diamond tool is directly imprinted on the cutting chip surface as a group of ‘ridges’. Through inspection of the locations, cross-sectional shape of these ridges by a 3D scanning electron microscope, the virtual cutting edge of the diamond tool under fracture wear is built up. A mathematical model was established to predict the virtual cutting edge with two geometric elements: semi-circle and isosceles triangle used to approximate the cross-sectional shape of ridges. Since the theoretical prediction of cutting edge profile concurs with the inspected one, the proposed tool wear monitoring method is found to be effective.     

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     

微铣削刀具磨损研究现状

在微制造领域,微铣削因具有加工材料的多样性和能实现三维曲面加工的独特优势而受到越来越多学者的关注,但是微铣刀的快速磨损严重影响了微铣削技术的应用.研究表明微铣刀的磨损主要发生在刀尖部位,刀具磨损呈现显著的尺度效应.分析了微铣刀的磨损机理、刀具磨损的影响因素和改善措施以及刀具磨损状态的监控,并指出了今后研究值得注意的发展方向.     

Effect of tool stiffness upon tool wear in high spindle speed milling using small ball end mill

Longer tool life can be tentatively achieved at a higher feed rate using a small ball end mill in high spindle speed milling (over several tens of thousands of revolutions per minute), although the mechanism by which tool life is improved has not yet been clarified. In the present paper, the mechanism of tool wear is investigated with respect to the deviation in cutting force and the deflection of a ball end mill with two cutting edges. The vector loci of the cutting forces are shown to correlate strongly with wear on both cutting edges of ball end mills having various tool stiffnesses related to the tool length. The results clarified that tool life can be prolonged by reducing tool stiffness, because the cutting forces are balanced, resulting in even tool wear on both cutting edges as tool stiffness is lowered to almost the breakage limit of the end mill. A ball end mill with an optimal tool length showed significant improvement in tool life in the milling of forging die models.     

高速轴向车铣45钢刀具磨损的研究

介绍了轴向车铣加工的特点,通过试验得到了高速轴向车铣加工45钢时刀具的磨损曲线,分析了在水冷和干切削时TiN涂层和金属陶瓷刀具的磨损特点,得出采用干切削更有利于延长刀具使用寿命,且TiN涂层刀具比金属陶瓷刀具更适合高速轴向车铣加工45钢.     

高硬度材料的超高速铣削

阐述高硬度材料超高速铣削技术的优点和应用,并重点讨论了其刀具系统和切削工艺参数等关键技术。     

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.     

Life prediction system using a tool’s geometric shape for high-speed milling

In recent years, machinery and tool technology has been developing rapidly. The accuracy of operations have also become more and more exact. Elsewhere, raw materials have also been honed, hoping to provide more useful properties than previously. Thus, how to find the best way to prolong the life of a tool subjected to hardened material cutting is the target of this research. Three kinds of tool angle of the endmill are used in this research; clearance angle, rake angle, and helical angle. The cutting conditions are the same; we only change the tool angle for all the cases studied. We attempt to discover better tool geometrical angles for the high-speed milling of NAK80 mold steel. The tool wear rate was measured through a toolmaker’s microscope and the roughness of the machined surface was measured by the roughness-measuring instruments after several complete surface layers were removed from the workpiece in the experiment. Also, a noise-mediator was used to detect the level of cutting noise during each surface layer workpiece removal of the high-speed milling process, and different noise levels were then compared with the tool wear rates for identifying noise characteristics in the case of an over-worn tool state. An abductive network was applied to synthesize the data sets measured from the experiments and the prediction models are established for tool-life estimation and over-worn situation alert under various combinations of different tool geometrical angles. Through the identification of tool wear and its related cutting noise, we hope to consequently construct an automatic tool wear monitoring system by noise detection during a high-speed cutting process to judge whether the tool is still good or not, and, so, the cost of milling can be reduced.     

超高速铣削及其在高硬度钢加工中的应用

阐述了高硬度钢超高速铣削的特点和应用,并讨论了其刀具系统、切削工艺参数和铣削方式等主要相关技术。     

Effects of cutting fluid application on tool wear in machining: Interactions with tool-coatings and tool surface features

Minimal Quantity of Lubricant (MQL) application of cutting fluids (CFs), or near-dry machining, is being proposed as an environmentally and economically viable alternative to conventional flooding under conditions where dry cutting is not feasible. However, several issues related to CF application effects on cutting tool wear need further clarification, especially, the interactions of CF application with tool-coatings and chip-breakers, both of which are widely employed in industrial cutting tools, need further study. This paper presents the results of an experimental study into the effects of different CF application methods on tool wear during machining of AISI 1045 steel using flat-faced and grooved, coated carbide cutting tools. The results provide insight into the mechanisms of tool wear in the presence of CFs, as well as the influence of chip-breaking geometric features, and tool-coating systems, on CF action. The wear mode was observed to be dictated by thermal considerations, rather than by any friction reduction capability of different CF application methods, and forced attempts at achieving lubricating action were negatively affecting tool life under some conditions.     

Analysis of force patterns and tool life in milling operations

The paper describes the practical effects of the operating parameters in the milling operation. Experiments have been conducted to measure cutting force and tool life under dry conditions. Based on the experimental results, three mathematical models have been developed: Force, TLife and Force/TLife. Further analyses have been conducted on the cutting force patterns: seasonal pattern and nonlinear trend. A process optimisation that is based on the minimum production cost has been applied to relate Force model, TLife model and machinability criteria, such as power consumption, cutting parameters and surface roughness.Nomenclature C _w cost of workpiece ($) - C _s set-up cost ($) - C _m machining cost ($) - C _o overhead cost ($) - C _r tool replacement cost ($) - C _t tool cost ($) - D diameter of the cutter (inch) - d depth of cut per pass (inch) - d ₀ required depth (inch) - e _t random error attth sample - F cutting force (N) - f feedrate (ipm) - L length of workpiece (inch) - N spindle speed (r.p.m.) - n number of teeth - P power of the motor (h.p.) - R surface roughness (µm) - R _e real part of a complex function - T tool life (min) - t sample number - t _m machining time (s) - t ₀ overhead time (s) - t _r tool replacement time (s) - t _s set-up time (s) - U _i unit cost of itemi ($/unit)v - v cutting speed (i.p.m.)     

Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation

A tool wear monitoring system is indispensable for better machining productivity, with the guarantee of machining safety by informing of the time due for changing a tool in automated and unmanned CNC machining. Different from monitoring methods using other signals, the monitoring of the spindle current has been used without requiring additional sensors on the machine tools. For reliable tool wear monitoring, only the current signal from tool wear should be extracted from the other parameters to avoid exhaustive analyses on signals in which all of the parameters are fused together. In this paper, the influences of force components from different parameters on the measured spindle current are investigated, and a hybrid approach to cutting force regulation is employed for tool wear signal extraction from the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects, varied to regulate the force component from tool wear.     

Tool wear analysis and improvement of cutting conditions using the high-pressure water-jet assistance when machining the Ti17 titanium alloy

This paper presents experimental results concerning the machinability of the titanium alloy Ti17 with and without high-pressure water jet assistance (HPWJA) using uncoated WC/Co tools. For this purpose, the influence of the cutting speed and the water jet pressure on the evolution of tool wear and cutting forces have been investigated. The cutting speed has been varied between 50 m/min and 100 m/min and the water jet pressure has been varied from 50 bar to 250 bar. The optimum water jet pressure has been determined, leading to an increase in tool life of approximately 9 times. Compared to conventional lubrication, an increase of about 30% in productivity can be obtained.     

基于复映测量的球头刀具磨损数据处理及其软件开发

在铣削加工中,刀具磨损对产品加工质量有重要的影响,以球头刀具磨损为研究对象,为了建立刀具磨损模型,采用复映测量手段获取刀具磨损,在复映测量获得刀具磨损的过程,针对复印测量的球头铣削刀具磨损数据提出了数据处理的方法,并以Visual C++为开发平台实现了基于复映测量的刀具磨损数据处理模块,为基于复映测量的刀具磨损建模提供了基础,所提出的刀具磨损数据处理方法可有效对刀具磨损数据进行分析建立刀具磨损模型。     

数控机床刀具磨损监测实验数据处理方法研究

数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.有针对性地回顾了国内外各种分析刀具磨损信号方法的研究工作,详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术的实现形式.通过比较各种数据处理方法的优缺点,提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来发展的主要方向.     

An intelligent sensor for monitoring milling cutter breakage

A new intelligent sensor system using neural networks to separate tool breakage clearly from the cutter run-out or cutting transients in milling is proposed. The features of the spindle displacement signal are fed into the input layer of the neural network. With the back propagation training algorithm, the output of the neural network can be used to identify the milling cutter with or without tool breakage. Experiments show that this new approach can monitor tool breakage in milling operations successfully.     

异形螺杆数控铣削刀具磨损智能建模研究

介绍了一种在线估算螺杆数控铣削中刀具磨损量的新方法。该方法基于螺杆铣削过程变切削参数的工况,提取了振动信号和功率信号的刀具磨损特征值,基于自适应神经—模糊推理系统建立了刀具磨损数学模型。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,可以较好的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。