APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.     

Predicting drill wear using an artificial neural network

The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high-speed steel (HSS) drill bits for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory.     

INTELLIGENT DETECTION OF DRILL WEAR

Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The neural network consisted of three layers: input, hidden, and output. The input vector comprised drill size, feed rate, spindle speed, and eight features obtained by processing the thrust and torque signals. The output was the drill wear state which either usable or failure. Drilling experiments with various drill sizes, feed rates and spindle speeds were carried out. The learning process was performed effectively by utilising backpropagation with smoothing and an activation function slope. The on-line detection of drill wear states using BPNs achieved 100% reliability even when the drill size, feed rate and spindle speed were changed. In other words, the developed on-line drill wear detection systems have very high robustness and hence can be used in very complex production environments, such as flexible manufacturing systems.     

麻花钻磨损特性的研究

通过对调质合金结构钢的大量钻削试验,研究了麻花钻磨损区的图形特征和磨损机理以及钻头失效与磨损图形参数、钻头切削寿命与钻削速度的关系。结果表明,麻花钻的磨损具有非线性特征,钻头转角和主刀刃及横刃区有两个显著不同的磨损区,随钻削速度的提高,这两个磨损区的特征差异及磨损带宽度之比明显增大。在钻削速度较低、钻头失效时,两个磨损区为较均衡的磨粒磨损和粘结磨损;钻削速度较高时,转角区剧烈的粘结磨损和氧化磨损使钻头加快失效,而主刀刃及横刃上的磨损却很小。受此影响,麻花钻的磨钝标准、耐用度问题较为复杂,钻头的寿命（Ｔ）－速度（Ｖ）曲线的泰勒特性范围很窄。     

Finite element and experimental investigation of temperature changes on a twist drill in sequential dry drilling

The drilling process is highly non-linear. Coupled with a thermo-mechanical machining, localized heating and temperature increases in the workpiece are caused by the rapid plastic deformation of the workpiece and by the friction along the drill-chip interface. The cutting temperature at the tool-chip interface is an important factor which directly affects workpiece surface integrity, tool wear, and hole diameter and cylindricity in the drilling process. In this study, the effects of sequential dry drilling operations on the drill bit temperature were investigated both experimentally and numerically. Drill temperatures were measured by inserting standard thermocouples into the coolant (oil) hole of TiN/TiAlN-coated carbide drills. Experimental studies were conducted using two different workpiece materials, AISI 1040 steel and Al 7075-T651. The drill bit temperature was predicted using a numerical computation with Third Wave AdvantEdge finite element method (FEM) software, which is based on Lagrangian explicit. The results obtained from the experimental study and finite element analyses (FEA) were compared. Reasonable agreement between the measured and calculated drill bit temperature results were found for sequential dry drilling.     

Chipping minimization in drilling ceramic materials with rotary ultrasonic machining

Ultrasonic machining (USM) has been considered as a new cutting technology that does not rely on the conductance of the workpiece. USM presents no heating or electrochemical effects, with low surface damage and small residual stresses on workpiece material, such as glass, ceramics, and others; therefore, it is used to drill microholes in brittle materials. However, this process is very slow and tool wear dependent, so the entire process has low efficiency. Therefore, to increase microhole drilling productivity or hole quality, rotary ultrasonic machining (RUM) is considered as a strong alternative to USM. RUM, which presents ultrasonic axial vibration with tool rotation, is an effective solution for improving cutting speed, precision, tool wear, and other machining responses beyond those of the USM. This study aims to reduce the microchipping or cracking at the exit of the hole, which inevitably occurs when brittle materials are drilled, with consideration of tool wear. To this end, response surface analysis and desirability functions are used for experimental optimization. The experimental results showed that the proposed RUM scheme is suitable for microhole drilling.     

基于小波包能量谱的HMM钻头磨损监测

从工程应用的角度论述了小波包分解原理及其能量谱监测理论，并将该理论应用于钻削力信号特征提取中，针对钻削过程特征矢量与钻头磨损之间具有较强的随机性和不确定性的特点，提出一种基于隐马尔可夫模型（HMM）的钻头磨损监测方法。实验结果表明，通过对钻削力信号进行多层小波包分解，提取各频段能量谱作为特征矢量可准确刻画工艺系统随钻头磨损的演化规律，利用HMM建立的各钻头磨损状态小波包能量谱的统计模型可有效跟踪钻头磨损的发展趋势，实现钻头磨损状态和寿命的监测。     

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.     

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.     

基于马尔可夫随机场工件表面纹理模型的刀具状态监测

基于马尔可夫随机场理论，建立了工件表面纹理图像的马尔可夫随机场纹理模型，并对工件表面纹理图像的特点进行了分析。在实验数据的基础上，对工件表面纹理图像的特征参数进行提取，提出采用相对距离作为刀具磨损程度的评价指标。指出三阶马尔可夫随机场能比较充分地反映工件表面纹理图像的特征。实验结果表明，基于马尔可夫随机场的工件表面纹理分析方法能够较好地适用于刀具状态监测。     

Cutting performance of an indexable insert drill for difficult-to-cut materials under supplied oil mist

In this study, the cutting performance of an indexable insert drill with an asymmetric geometry for cutting difficult-to-cut materials was investigated. A solid twist drill with a symmetric geometry was used to compare the cutting characteristics. The cutting characteristics were evaluated using the thrust force, inner-surface roughness of the drilled hole, wear behavior, and tool temperature. Workpieces made of stainless steel, titanium alloy, and nickel-based alloy were selected as difficult-to-cut materials, and carbon steel was also selected. The tool temperature was higher in the order of carbon steel, stainless steel, titanium alloy, and nickel-based alloy for every drill under minimum quantity lubrication cutting. The influence of the workpiece material on the thrust force was different from that of the tool temperature for the indexable insert drill, whereas that of the solid twist drill was similar to the tool temperature tendency. When cutting the titanium alloy and nickel-based alloy, the tool temperature and thrust force of the indexable insert drill were lower than those of the solid-type twist drill. The inner-surface roughness of a hole drilled with the indexable insert drill had almost the same quality as that of a hole drilled with the solid-type twist drill when cutting the difficult-to-cut materials. The wear behavior of the indexable insert drill was remarkably different from that of the solid-type twist drill, and the flaking of the coating and the abrasion wear at the rake face were notable in the indexable insert drill.     

Performance and failure of high speed steel drills related to wear

Three high speed steel grades, representing low, medium and high contents of alloying elements, were investigated in a comparative drill performance test using two different work materials. The results are discussed with reference to the observed wear mechanisms. Since drill performance is usually expressed as number of holes to failure, relationships between gradual wear and final failure of the drills are emphasized.The performance tests resulted in chisel edge, crater, flank and margin wear. The corresponding wear mechanisms were studied with the aid of scanning electron and light optical microscopy. The most important mechanisms are (1) abrasive wear for drilling in a plain carbon steel and (2) adhesive wear for drilling in a quenched and tempered steel.     

高速钢麻花钻的磨损试验研究

麻花钻是实现孔加工的重要工具,然而切削刃口的快速磨损是制约钻孔质量和钻头寿命的重要因素。基于产品抽样钻孔试验与正交试验,本文对高速钢麻花钻的磨损、破损等失效形式进行了综合分析,探讨了以钻孔直径为评价指标时的孔加工数量与孔径的关系,并对麻花钻磨损的关键影响因素进行了正交试验研究。结果表明,麻花钻磨损与破损形式主要有主切削刃前刀面与后刀面磨损、横刃磨损、刃带磨损、外圆转角磨损、崩刃等;切削速度对麻花钻磨损影响最大、进给量次之、孔深影响最小;此外,随着加工孔数量的增加,孔径呈减小趋势。     

PCBN刀具的磨损机理和干切削GCr15时的磨损与寿命

研究了PCBN刀具的磨损机理和磨损形式。通过对干切削GCr15轴承钢时刀具的磨损形式及寿命进行试验研究 ,得出了工件硬度、切削速度对PCBN刀具磨损的影响规律以及工件硬度在临界硬度附近时刀具磨损速度最快的结论。加工两种硬度工件时的刀具寿命方程表明 ,切削速度对PCBN刀具寿命的影响小于对硬质合金及陶瓷刀具寿命的影响。     

FUZZY RADIAL BASIS FUNCTION (FRBF) NETWORK BASED TOOL CONDITION MONITORING SYSTEM USING VIBRATION SIGNALS

Thriving automation in industries leads to more research on the tool condition monitoring systems for better accuracy and fast recognition/evaluation of tool wear. Research on the applicability of the new advances in the soft-computing as well as in the signal processing fields is the inevitable consequence. In this work, a new soft-computing modeling technique, fuzzy radial basis function (FRBF) network has been applied to the prediction of drill wear using the vibration signal features. This work presents the wear prediction performance comparison of this new model with three other already tried and established soft-computing models, such as back propagation neural network (BPNN), radial basis function network (RBF) and normalized radial basis function network (NRBF), for both time-domain as well as wavelet packet approaches of feature extraction. Experimental results show that FRBF model with wavelet packet approach produces the best performance of predicting flank wear.     

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic appli-cations of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bot-tom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regressior layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effective-ness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.     

WEAR MONITORING IN TURNING OPERATIONS USING VIBRATION AND STRAIN MEASUREMENTS

For the efficient and reliable operation of automated machining processes, the implementation of suitable tool condition monitoring (TCM) strategy is required. Various monitoring systems, utilising sophisticated signal processing techniques, have been widely researched for a number of different processes. Most monitoring systems developed up to date employ force, acoustic emission and vibration, or a combination of these and other techniques with a sensor integration strategy. With this work, the implementation of a monitoring system utilising simultaneous vibration and strain measurements on the tool tip, is investigated for the wear of synthetic diamond tools which are specifically used for the manufacturing of aluminium pistons. Contrary to many of the earlier investigations, this work was conducted in a manufacturing environment, with the associated constraints such as the impracticality of direct measurement of the wear. Data from the manufacturing process was recorded with two piezoelectric strain sensors and an accelerometer, each coupled to a DSPT Siglab analyser. A large number of features indicative of tool wear were automatically extracted from different parts of the original signals. These included features from the time and frequency domains, time-series model coefficients (as features) and features extracted from wavelet packet analysis. A correlation coefficient approach was used to automatically select the best features indicative of the progressive wear of the diamond tools. The self-organising map (SOM) was employed to identify the tool state. The SOM is a type of neural network based on unsupervised learning. A near 100% correct classification of the tool wear data was obtained by training the SOM with two independent data sets, and testing it with a third independent data set.