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.     

A dynamic multi-scale Markov model based methodology for remaining life prediction

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.     

CHMM for tool condition monitoring and remaining useful life prediction

To solve the problems of tool condition monitoring and prediction of remaining useful life, a method based on the Continuous Hidden Markov Model (CHMM) is presented. With milling as the research object, cutting force is taken as the monitoring signal, analyzed by wavelet packet theory to reduce noise and extract the energy feature of the signal as a basis for diagnosis. Then, CHMM is used to diagnose tool wear state. Finally, a Gaussian regression model is proposed to predict the milling tool’s remaining useful life after the test sample data are verified to be consistent with the Gaussian distribution based on a reliable identification of the milling tool wear state. The probability models of tool remaining useful life prediction could be established for tools with different initial states. For example, when an unknown state of milling force signal is delivered to the milling tool online diagnostic system, the state and the existing time of this state could be predicted by the established prediction model, and then, the average remaining useful life from the present state to the tool failure state could be obtained by analyzing the transfer time between each state in the CHMM. Compared to the traditional probabilistic model, which requires a large amount of test samples, the experimental cost is effectively reduced by applying the proposed method. The results from the experiment indicate that CHMM for tool condition monitoring has high sensitivity, requires less training samples and time, and produces results quickly. The method using the Gaussian process to accurately predict remaining life has ample potential for application to real situations.     

A review of flank wear prediction methods for tool condition monitoring in a turning process

Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.     

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.     

铣刀磨损量监测和剩余寿命预测方法研究

研究在线诊断端面铣刀磨损量和预测铣刀剩余寿命的方法。首先通过实验采集Y向铣削力作为监测信号,分析铣削力与刀具磨损量VB和刀具磨损时间之间的关系。然后,通过铣削力信号分析提取出有效监测特征向量,此特征向量作为BP神经网络的输入,用于刀具磨损量的监测和剩余寿命的预计。最后,通过实验证明,该神经网络模型误差很小,利用该方法能够正确地进行在线监测和预测。     

Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach

According to the Taylor tool life equation, tool life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic equations, there is inherent uncertainty in the empirical constants and tool life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty.     

Continuous tool wear prediction based on Gaussian mixture regression model

The prediction of continuous tool wear process plays an important role in realizing adaptive control and optimizing manufacturing process so as to improve production efficiency and quality of the workpiece. However, the complexity of the tool wear process and the unpredictable disturbance during milling process make it difficult to realize robust and accurate estimation of the tool wear value. In this paper, the Gaussian mixture regression (GMR) model is proposed to realize continuous tool wear prediction based on features extracted from cutting force signal. The main characteristic of the GMR model is that the relationship between the tool wear value and the features is built by the combination of the Gaussian mixture model in which the variation of the training data is described by the probability density of the Gaussian distribution, and the wild data can be abandoned if its probability is small enough. To test the effectiveness of the proposed method, the experiment of titanium alloy milling was carried out, and the spectrum peak value corresponding to the harmonic of tooth passing frequency was extracted as the explanatory variables to predict the tool wear value. In addition, multiple linear regression, radius basis function, and back propagation neural network are also adopted to make a comparison with the GMR model. The analysis of four performance criteria shows that the GMR-based method is the most accurate among these methods.     

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.     

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.     

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.     

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.     

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.     

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.     

基于PCA和动态监测模型的刀具寿命在线检测技术

为了实现机械加工过程中刀具寿命在线准确识别,采用时域、频域和小波变换等信号分析方法,提取切削力信号和振动信号中与刀具寿命变化敏感的多个特征,系统输入特征向量通过主向量分析(PCA)方法根据累积贡献率进行优化选择;监测系统根据加工条件自动选择对应的,由两个寿命计算模型构成的动态监测模型,两个模型根据输出精度交替实现刀具寿命计算、在线学习和模型参数更新,最终实现了刀具寿命的在线预测。长期运行结果证明,建立的刀具寿命监测系统能够准确预测刀具的寿命状态,具有良好的自学习能力,在线计算速度高,具有较强的工业推广价值。     

A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains. This paper presents an age-dependent hidden semi-Markov model (HSMM) based prognosis method to predict equipment health. By using hazard function (h.f.), CBM is based on a failure rate which is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in CBM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The previous HSMM based prognosis algorithm assumed that the transition probabilities are only state-dependent, which means that the probability of making transition to a less healthy state does not increase with the age. In the proposed method, in order to characterize the deterioration of equipment, three types of aging factors that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are integrated into the HSMM. With an iteration algorithm, the original transition matrix obtained from the HSMM can be renewed with aging factors. To predict the remaining useful life (RUL) of the equipment, hazard rate is introduced to combine with the health-state transition matrix. With the classification information obtained from the HSMM, which provides the current health state of the equipment, the new RUL computation algorithm could be applied for the equipment prognostics. The performances of the HSMMs with aging factors are compared by using historical data colleted from hydraulic pumps through a case study.     

Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.     

Investigated iterative convergences of neural network for prediction turning tool wear

This study investigates the iterative convergences of neural network for prediction turning tool wear. For the smart manufacturing, the intelligent predict     

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.