Model development for tool wear effect on AE signal generation in micromilling

A model for the relation between the acoustic emission signal generation and tool wear was established for cutting processes in micromilling by considering the acoustic emission (AE) generation and propagation mechanisms. In addition, the effect of tool wear on the AE signal generation in frequency and amplitude was studied. In the model development, the finite element analysis was first used to calculate the shear strain rate distribution on the shear plane based on the orthogonal cutting assumption. Conversely, the contact stress distribution of workpiece on the flank wear face was established based on the Waldorf model. Following the finite element method, the dislocation density in materials was calculated based on Orowan’s law with the calculated stress rate. Finally, the AE signal detected by the sensor was calculated by considering the Gaussian probability density function for the distribution of AE source on the shear plane and the one-dimension wave equation for AE signal propagation. Based on the developed model, the effect of tool wear on the AE signal generation was investigated and compared to the experimental results. The results obtained from these investigations indicate that the proposed model can be used to predict the effect of tool wear on the AE signal generation.     

数控机床刀具磨损监测方法研究

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.文章有针对性地回顾了国内外各种刀具磨损监测方法的研究工作,详细叙述了切削力监测法、切削噪声监测法、功率监测法、声发射监测法、电流监测法以及基于多传感器监测法等六种刀具磨损监测方法.本文通过比较各种监测方法的优缺点,提出基于多传感器监测法是数控机床刀具磨损监测方法的未来发展的主要方向.     

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.     

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.     

数控车削加工过程的刀具磨损动态监测

对车削加工刀具磨损的各阶段信号进行采集,通过动态时频域分析,找到车削加工过程中刀具磨损的重要参数变化,对其进行振铃记数和人工神经网络的模式识别,实现了车削加工刀具磨损的状态检测。     

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.     

Size effect and tool geometry in micromilling of tool steel

The market for freeform and high quality microdies and moulds made of steel is predicted to experience a phenomenal growth in line with the demand for microsystems. However, micromachining of hardened steel is a challenge due to unpredictable tool life and likely differences in process mechanism compared to macro-scale machining. This paper presents an investigation of the size effect in micromilling of H13 hardened tool steel. In this case, the size effect in micromilling hardened tool steel was observed by studying the effect of the ratio of undeformed chip thickness to the cutting edge radius on process performance. The paper explores how this ratio drives the specific cutting force, surface finish and burr formation in micro-scale machining. In addition, the effect of different microend mill geometry on product quality was explored. The paper provides a valuable insight into optimum micro-scale machining conditions for obtaining the best surface finish and minimizing burr size.     

Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining

The International Journal of Advanced Manufacturing Technology - Hybrid structures of metals and composite materials are increasingly common in aerospace industry, and the optimization and...     

Use of process signals for tool wear progression sensing in drilling small deep holes

Detailed knowledge about the relation between wear progression of a cutting tool and the cutting forces generated is of paramount importance for the development of a tool condition monitoring strategy. This paper discusses the changes in the different process signals with progressing tool wear of small diameter twist drills (D=1.5 mm), when drilling boreholes having a depth of 10 times the diameter in plain carbon steel using MQL. The effect of different wear patterns on the process signals is presented. Furthermore, several features, which evolve over the life of the drills, are identified and extracted from the process signals. Knowledge about the evolution of these features can support the user to determine the final tool life stage, so that the drill can be replaced before the final fracture occurs.     

On-line monitoring of tool wear in turning operation in the presence of tool misalignment

A vision system using high-resolution CCD camera and back-light was developed for the on-line measurement of nose wear of cutting tool inserts. Initial study showed that the system is sensitive to several factors in the work environment such as misalignment of cutting tool, presence of micro-dust particles, vibration and intensity variation of ambient light. An algorithm using Wiener filtering, median filtering, morphological operations and thresholding was developed to decrease the system error caused by these factors. A conforming method was used to overcome misalignment of the tool insert during offline and on-line measurement. The algorithm, combined with a subtraction method, was applied to measure the nose wear area of the inserts under different machining conditions.     

Cutting tool wear monitoring for reliability analysis using proportional hazards model

Electrochemical machining (ECM) is an important technology in machining difficult-to-cut materials and to shape free-form surfaces. In ECM, material is removed by electrochemical dissolution process, so part is machined without inducing residual stress and without tool wear. To improve technological factors in electrochemical machining, introduction of electrode tool ultrasonic vibration is justifiable. This method is called as ultrasonically assisted electrochemical machining (USAECM). In the first part of the paper, the analysis of electrolyte flow through the gap during USAECM has been presented. Based on computational fluid dynamic methods, multiphase, turbulent and unsteady electrolyte flow between anode and cathode (under assumption that cavitation phenomenon occurs) has been analysed. Discussion of the obtained solutions is the base to define optimal conditions of electrolyte flow in case of USAECM process. The second part of the paper is connected with experimental investigations of USAECM process. Classic experimental verification of obtained results in case of machining is extremely difficult, but influence of the ultrasonic vibration can be observed indirectly by changes in technological factors (in comparison to machining without ultrasonic intensification), whereas results of numerical simulation give possibility to understand reason and direction of technological factors changes. Investigations proved that ultrasonic vibrations change conditions of electrochemical dissolution and for optimal amplitude of vibration gives possibility to decrease the electrode polarisation.     

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700?μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30?Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.     

A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction

Tool wear is one of the important indicators to reflect the health status of a machining system. In order to obtain tool’s wear status, tool condition monitoring (TCM) utilizes advanced sensor techniques, hoping to find out the wear status through those sensor signals. In this paper, a novel weighted hidden Markov model (HMM)-based approach is proposed for tool wear monitoring and tool life prediction, using the signals provided by TCM techniques. To describe the dynamic nature of wear evolution, a weighted HMM is first developed, which takes wear rate as the hidden state and formulates multiple HMMs in a weighted manner to include sufficient historical information. Explicit formulas to estimate the model parameters are also provided. Then, a particular probabilistic approach using the weighted HMM is proposed to estimate tool wear and predict tool’s remaining useful life during tool operation. The proposed weighted HMM-based approach is tested on a real dataset of a high-speed CNC milling machine cutters. The experimental results show that this approach is effective in estimating tool wear and predicting tool life, and it outperforms the conventional HMM approach.     

The milling tool wear monitoring using the acoustic spectrum

In the present study, the tool wear has been monitored using the cutting sound acoustic spectrum and the linear predictive cepstrum coefficient (LPCC) of the milling sound signal would be extracted to be used as the acoustic spectrum characteristic parameters. The relationship between each order component of LPCC and the flank wear of the tools was analysed. The experimental results show that there are clear characteristic components in the milling sound signal related to the tool wear. It has been found that the characteristic components associated with tool wear are mainly concentrated in the sixth-, seventh- and eighth-order components of LPCC.     

Fuzzy c-means clustering based colour image segmentation for tool wear monitoring in micro-milling

Micro-milling is an extensively used micro-machining process for producing high precision 3D components from varied materials. However, tool wear in micro-tools is a big concern, as component accuracy directly depends on it. Also, size effects limit the monitoring by the naked eye, but it can be compensated by implying a proper wear monitoring mechanism. Various direct and indirect methods have earlier been used for monitoring purposes, and considering the needs of the fourth industrial revolution, one of the direct methods, machine vision, when combined with image processing algorithms, can play a more prominent role. Current work focuses on creating a wear monitoring algorithm based on fuzzy c-means clustering technique directly implied on acquired colour micro-tool images. The proposed algorithm has three steps: the first step is Region of Interest (ROI) extraction, where the background is removed, orientation correction is done, and ROI on each tooth is extracted from micro-tool colour images. The second uses the fuzzy c-means technique on ROI to cluster them, from which wear cluster is chosen and morphologically enhanced. The last step performs pixel level measurement and results in numerical wear width. Overall, quantitative results at each step are correlation coefficient of 99 % after image registration, segmentation accuracy of 92 % and wear measurement accuracy of 97 %. A comparison is also made between the proposed algorithm, k-means clustering and RGB thresholding technique, where the proposed algorithm outshines. Lastly, the wear measurement error of the proposed algorithm is less than 5 %, indicating its repeatable, reliable, and robust nature.     

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.     

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.     

基于模糊聚类的数控车削加工刀具磨损检测研究

将模糊聚类分析原理应用于数控车削加工刀具磨损检测,对数控车削加工刀具磨损的各阶段力信号和振动信号进行采集,通过小波滤波及功率谱的谱分析,找到车削加工过程中刀具磨损的典型参数变化。通过提取信号特征值进行模糊聚类,实现了数控车削加工刀具磨损的状态识别。     

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.