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.     

PSO优化灰色神经网络的珩磨油石磨损预测

为了预测油石的切削寿命,保证珩磨加工质量,引入灰色神经网络,通过将珩磨工艺加工参数作为模型输入来预测油石的磨损量,最终建立了珩磨油石磨损量预报模型。在油石磨损量预测过程中,针对神经网络存在收敛速度慢、容易陷入局部最优等缺陷,利用粒子群算法对灰色神经网络的参数进行优化。试验结果表明,基于粒子群算法改进的灰色神经网络具有更好的逼近能力和预测精度,便于合理更换油石。     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

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.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

基于气体传感器阵列的混合气体定量分析

优选CO和H2气体敏感的半导体气体传感器组成阵列，建立实时数据采集系统，结合BP神经网络模式识别技术，实现了混合气体组分的定量分析。讨论了不同响应时间下的阵列输出值、不同的数据预处理算法及不同的神经网络结构等主要影响因素对网络输出结果的影响。结果表明，采用RRD预处理算法对3min响应时间下的阵列输出值进行处理，再输入到有12个隐层神经元数的3层BP神经网络进行训练，预测的效果最好。该处理模式能较准确地完成CO和H2混合气体组分的定量分析。     

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.     

基于双信号融合的超声振动钻削钻头磨损状态监测研究

为了监测超声振动钻削过程中钻头的磨损状态,构建了超声振动钻削钻头的振动信号和AE信号的采集系统,通过采集不同磨损状态下钻头的振动信号和AE信号,对其进行小波分解,得到与钻头磨损相关的特征值,将二者融合后作为神经网络的输入,输入至构建的12-10-3的BP神经网络中,进行钻头磨损状态的识别。试验结果表明,所建BP神经网络通过振动和AE的融合信号对钻头的有效识别率为91.7%,可以有效对钻头的磨损状态进行识别。     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

TOOL CONDITION MONITORING USING ACOUSTIC EMISSION,SURFACE ROUGHNESS AND GROWING CELL STRUCTURES NEURAL NETWORK

The monitoring of tool wear is a most difficult task in the case of various metal-cutting processes. Artificial Neural Networks (ANN) has been used to estimate or classify certain wear parameters, using continuous acquisition of signals from multi-sensor systems. Most of the research has been concentrated on the use of supervised neural network types like multi-layer perceptron (MLP), using back-propagation algorithm and Radial Basis Function (RBF) network. In this article, a new constructive learning algorithm proposed by Fritzke, namely Growing Cell Structures (GCS) has been used for tool wear estimation in face milling operations, thereby monitoring the condition of the tool. GCS generates compact network architecture in less training time and performs well on new untrained data. The performance of this network has been compared with that of another constructive learning algorithm-based neural network, namely the Resource Allocation Network (RAN). For the sake of establishing the effectiveness of GCS, results obtained have been compared with those obtained using Multi Layer Perceptron (MLP), which is a standard and widely used neural network.     

基于模糊神经网络的深孔加工刀具磨损率预测

刀具的过快磨损不仅增大加工成本,也影响工件的最终加工质量,因此预测和减少刀具磨损率具有重要意义。由于BP神经网络本身容易陷入局部极小值、收敛速度慢等缺陷,且深孔加工过程及其复杂,无法建立加工中刀具磨损率与加工参数之间的准确数学模型,故采用模糊神经网络建立BTA刀具磨损率在线钻削模型。仿真和实验结果表明,该模型能有效预测BTA刀具磨损率,对提高刀具寿命和加工深孔的质量具有一定的意义。     

基于电流信号钻头磨损状态预报系统

介绍了一个钻头磨损状态在线预报系统。通过监测主电机电流信号建立神经网络动态预报模型 ,对钻头后刀面磨损量分类建立在不同磨损类别下的神经网络模型 ,以神经网络模型估算的电流值作为模糊聚类中心 ,根据预报电流值对钻头磨损状态进行模糊分类 ,从而预报磨损状态。实验表明 ,此预报系统具有较高的成功率和可靠性     

钢坯拉速模糊信息粒化及钢坯定重切割的极限学习机预报

为提高钢坯定重切割精度,分析了钢坯质量与钢坯平均拉速间的关联性。建立了钢坯拉速数据的模糊信息粒化模型,将每5根钢坯的平均拉速数据变换为一个三角型模糊粒,得到模糊粒子中的3个参数：钢坯平均拉速变化的最小值vLow、均值vmid和最大值vup,降低钢坯拉速数据的复杂度,得到含不同信息的拉速数据粒化子集。建立了基于信息粒化数据的支持向量机（SVM）回归模型,以模糊粒子参数为输入向量对钢坯平均拉速进行回归预测,得到下一根钢坯的平均拉速预测值。综合考虑钢坯截面积、钢坯平均拉速、定尺长度、下一根钢坯平均拉速预测值等影响因素,建立了极限学习机（ELM）神经网络预报模型,实现了钢坯定重预报。     

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.     

车削过程切削力的计算机数值仿真

切削力是表征切削过程最重要特征的物理量，其动态变化将直接影响加工过程中刀具与工件的相对位移、刀具磨损和表面加工质量等，所以对切削力建模是进行加工过程物理仿真研究的基础。因此在基于实时工况的切削实验研究基础上，考虑切削参数的因素，利用BP（back pmpagation）神经网络建立车削过程中的切削力的仿真模型。通过大量的样本训练，使神经网络能够对切削力进行较准确地数值仿真。     

Analysis of Sliding Wear Characteristics of BFS Filled Composites Using an Experimental Design Approach Integrated with ANN

Short fiber-reinforced polymer composites are used in numerous tribological applications. In the present work, an attempt was made to improve the wear resistance of short glass fiber (SGF)-reinforced epoxy composites by incorporation of microsized blast furnace slag (BFS) particles. The effect of various operational variables and material parameters on the sliding wear behavior of these composites was studied systematically. The design of experiments approach using Taguchi's orthogonal arrays was used. This systematic experimentation led to identification of significant variables that predominantly influence the wear rate. The Taguchi approach enabled us to determine optimal parameter settings that led to minimization of the wear rate. The morphology of worn surfaces was then examined by scanning electron microscopy and possible wear mechanisms are discussed. Further, in this article, the potential of using artificial neural networks (ANNs) for the prediction of sliding wear properties of polymer composites is explored using an experimental data set generated from a series of pin-on-disc sliding wear tests on epoxy matrix composites. The ANN prediction profiles for the characteristic tribological properties exhibited very good agreement with the measured results, demonstrating that a well-trained network was created. The simulated results explaining the effect of significant process variables on the wear rate indicated that the trained neural network possessed enough generalization capability to predict wear rate from any input data that are different from the original training data set.     

A step towards the in-process monitoring for electrochemical microdrilling

The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6?×?9?×?1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L ₂₇. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.     

Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.     

基于改进BP网络的电火花加工工艺选择模型

提出了基于对数变换的数据预处理改进算法,测试表明效果较好。以加工面积、电极损耗比、表面粗糙度为输入参数,脉冲电流、脉冲宽度、脉冲间隙、放电间隙、伺服基准、伺服速度、加工速度为输出参数,提出了基于改进BP神经网络的电火花加工工艺选择模型。经过与实验数据的比较,该模型能真实反映机床的加工工艺规律,能实现在给定加工条件下进行电加工参数的自动选择。