Tool wear monitoring in drilling using force signals

Utilization of force signals to achieve on-line drill wear monitoring is presented in this paper. A series of experiments were conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals and to establish the relationship between force signals and tool wear as well as other cutting parameters when drilling copper alloy. These experiments involve four independent variables; spindle rotational speed ranging from 600 to 2400 rev min⁻¹, feed rate ranging from 60 to 200 mm min⁻¹, drill diameter ranging from 5 to 10 mm, and average flank wear ranging from 0.1 to 0.9 mm. A statistical analysis provided good correlation between average thrust and drill flank wear. The relationship between cutting force signals and cutting parameters as well as tool wear is then established. The relationship can then be used for on-line drill flank wear monitoring. Feasibility studies show that the use of force signal for on-line drill flank wear monitoring is feasible.     

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.     

Feedrate optimization for variant milling process based on cutting force prediction

Machining process modeling, simulation and optimization is one of the kernel technologies for virtual manufacturing (VM). Optimization based on physical simulation (in contrast to geometrical simulation) will bring better control of a machining process, especially to a variant cutting process – a cutting process so complex that cutting parameters, such as cutting depth and width, change with cutter positions. In this paper, feedrate optimization based on cutting force prediction for milling process is studied. It is assumed that cutting path segments are divided into micro-segments according to a given computing step. Heuristic methods are developed for feedrate optimization. Various practical constraints of a milling system are considered. Feedrates at several segments or micro-segments are determined together but not individually to make milling force satisfy constraints and approach an optimization objective. After optimization, an optimized cutting location data file is outputted. Some computation examples are given to show the optimization effectiveness. This revised version was published online in October 2004 with a correction to the issue number.     

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.     

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 .     

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.     

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.     

Cutting force model considering tool edge geometry for micro end milling process

The analysis of the cutting force in micro end milling plays an important role in characterizing the cutting process, as the tool wear and surface texture depend on the cutting forces. Because the depth of cut is larger than the tool edge radius in conventional cutting, the effect of the tool edge radius can be ignored. However, in micro cutting, this radius has an influence on the cutting mechanism. In this study, an analytical cutting force model for micro end milling is proposed for predicting the cutting forces. The cutting force model, which considers the edge radius of the micro end mill, is simulated. The validity is investigated through the newly developed tool dynamometer for the micro end milling process. The predicted cutting forces were consistent with the experimental results.     

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.     

基于主轴电流的铣削力间接监测方法

实时准确地监测铣削状态对于提高加工质量与加工效率具有重要意义,切削力作为重要的加工状态监测对象,因其监测设备昂贵且安装不便而受到限制,为此提出一种考虑刀具磨损的基于主轴电流的铣削力监测方法.首先基于切削微元理论建立了考虑后刀面磨损的铣削力模型,并通过铣削实验进行铣削力模型系数标定;然后对主轴电流与铣削力的关系进行理论建...     

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.     

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.     

On-machine measurement of microtool wear and cutting edge chipping by using a diamond edge artifact

This paper presents precision on-machine measurement of microwear and microcutting edge chipping of the diamond tool used in a force sensor integrated fast tool servo (FS-FTS) mounted on a three-axis diamond turning machine. A diamond edge artifact with a nanometric sharpness is mounted on the machine spindle with its axis of rotation along the Z-axis to serve as a reference edge artifact. The diamond tool is placed in the tool holder of the FS-FTS to generate cutting motion along the Z-axis. By moving the X-slide on which the FS-FTS is mounted, the reference edge can be scanned by the diamond tool. During the scanning, the Z-directional position of the tool is closed-loop controlled by the FS-FTS in such a way that the contact force between the tool tip and the reference edge is kept constant based on the force sensor output of the FS-FTS. The tool edge contour can be obtained from the scan trace of the tool tip, whose X- and Z-directional coordinates are provided by the output of the linear encoder of the X-slide and that of the displacement sensor in the FS-FTS, respectively. Since the reference edge artifact has a good hardness and a nanometric sharpness to ensure the lateral resolution of measurement, a microwear on the cutting edge of the diamond tool can be indentified from the measured tool edge contour. Experiments of on-machine measurement of tool edge contour and microtool wear are carried out to demonstrate the feasibility of the proposed system.     

Extracting cutting constants via harmonic force components for a general helical end mill

Mechanistic cutting constants serve well in predicting milling forces, monitoring the milling process as well as in helping to understand the mechanistic phenomena of a machining process for a unique pair of workpiece and cutter materials under various types of cutting edge geometry. This paper presents a unified approach in identifying the six shearing and ploughing cutting constants for a general helical end mill from the dynamic components of the measured milling forces in a single cutting test. The identification model is first presented assuming the milling force is measured with a known phase angle of the cutter spindle. When the phase angle of the cutter rotation is not available, as is the case for most milling machines, it is shown that the true phase angle can be identified through the theoretical phase relationship between the different harmonic components of the milling forces measured with an arbitrary phase angle. The numerical simulation and the experimental results for ball and cylindrical end mills are presented to demonstrate and validate the identification methods.     

An intelligent sensor system approach for reliable tool flank wear recognition

An intelligent sensor system approach for reliable flank wear monitoring in turning is described. Based on acoustic emission and force sensing, an intelligent sensor system integrates multiple sensing, advanced feature extraction and information fusion methodology. Spectral, statistical and dynamic analysis have been used to determine primary features from the sensor signals. A secondary feature refinement is further applied to the primary features in order to obtain a more correlated feature vector for the tool flank wear process. An unsupervised ART2 neural network is used for the fusion of AE and force information and decision-making of the tool flank wear state. The experimental results confirm that the developed intelligent sensor system can be reliably used to recognise the tool flank wear state over a range of cutting conditions.Notation mean - ² variance - k _n end condition factor of the cantilever beam - E Young's modulus of tool holder - I moment of inertia of tool holder at cross section - m mass of tool holder per unit length - L length of tool overhang - l the size of the moving window - fm, pm, sm, km the mean values of the four primary features (the tangential force component, the frequency band power, the skew, and the kurtosis) - fs, ps, ss, ks the standard deviation values of the four primary features - F= resultant feature vector ART2 neural network parameters I _i element of input vector - Y _i output node - W _i ,X _i ,U _i ,V _i ,P _i ,Q _i parameters inF ₁ layer - R _i orienting parameter - vigilance parameter - b _ij ,t _ji bottom-to-top and top-to-bottom weights - a, b, c network parameters - f() thresholding function     

Optimization of cutting conditions for minimum residual stress,cutting force and surface roughness in end milling of S50C medium carbon steel

Residual stresses are usually imposed on a machined component due to thermal and mechanical loading. Tensile residual stresses are detrimental as it could shorten the fatigue life of the component; meanwhile, compressive residual stresses are beneficial as it could prolong the fatigue life. Thermal and mechanical loading significantly affect the behavior of residual stress. Therefore, this research focused on the effects of lubricant and milling mode during end milling of S50C medium carbon steel. Numerical factors, namely, spindle speed, feed rate and depth of cut and categorical factors, namely, lubrication and milling mode is optimized using D-optimal experimentation. Mathematical model is developed for the prediction of residual stress, cutting force and surface roughness based on response surface methodology (RSM). Results show that minimum residual stress and cutting force can be achieved during up milling, by adopting the MQL-SiO₂ nanolubrication system. Meanwhile, during down milling minimum residual stress and cutting force can be achieved with flood cutting. Moreover, minimum surface roughness can be attained during flood cutting in both up and down milling. The response surface plots indicate that the effect of spindle speed and feed rate is less significant at low depth of cut but this effect significantly increases the residual stress, cutting force and surface roughness as the depth of cut increases.     

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system.     

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