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.     

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...     

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.     

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.     

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.     

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。     

改进灰色模型在刀具状态监控中的研究与应用

刀具状态的精确监控是保证金属切削加工过程顺利进行的关键,因此研制准确、可靠且成本低廉的刀具磨损状态监控系统一直是研究人员所追求的目标.引人改进灰色预测模型理论用来预测·刀具的运行状态,具有所需数据少、精度高的优势.预测曲线符合实际,较好地反映了刀具磨损状态的变化,达到了监控的目的.     

Linear discriminant function analysis of acoustic emission signals for cutting tool monitoring

A principal setback to automation of the machining process is the inability to completely monitor the condition of the cutting tool in real time. Whereas several of the techniques developed to date are useful in specific applications, no universally applicable sensor is yet available.Acoustic emission is one of the most promising techniques to be recently developed for on-line cutting tool monitoring. However, signal analysis is still an area that requires further investigation to enhance the potential of acoustic emission. For this purpose, frequency-based pattern recognition concepts using linear discriminant functions have been used in analysing acoustic emission signals generated during machining to distinguish between different signal sources, specifically chip formation, tool fracture, and chip noise. Five features were used for classification in the frequency range of 100 kHz to 1 MHz, with each feature consisting of a 20 kHz bandwidth, and were selected using the class mean scatter criterion. The coefficients of the discriminant functions were obtained by training the system using signals generated by each of the sources of interest. An AISI 1018 steel was machined using a titanium carbide-coated cutting tool. Cutting speeds ranged from 200 to 800 ft/min (1 to 4 m/sec) with feed rats of 0·0005 to 0·0075 in/rev (0·0133 mm/rev to 0·191 mm/rev) and depth of cut 0·17 in (4·32 mm). The results show a successful classification rate of 90% for tool breakage, while those for chip formation and chip noise were 97 and 86% respectively.     

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.