Tool condition monitoring using reflectance of chip surface and neural network

A wide variety of tool condition monitoring techniques has been introduced in recent years. Among them, tool force monitoring, tool vibration monitoring and tool acoustics emission monitoring are the three most common indirect tool condition monitoring techniques. Using multiple intelligent sensors, these techniques are able to monitor tool condition with varying degrees of success. This paper presents a novel approach for the estimation of tool wear using the reflectance of cutting chip surface and a back propagation neural network. It postulates that the condition of a tool can be determined using the surface finish and color of a cutting chip. A series of experiments has been carried out. The experimental data obtained was used to train the back propagation neural network. Subsequently, the trained neural network was used to perform tool wear prediction. Results show that the prediction is in good agreement with the flank wear measured experimentally.     

On-line monitoring of boring tools for control of boring operations

In order to carry out precision and quality control of boring operations, on-line monitoring of boring tools is essential. Fourteen features were extracted by processing cutting force signals using virtual instrumentation. A Sequential Forward Search (SFS) algorithm was employed to select the best combination of features. Backpropagation neural networks (BPNs) and adaptive neuro-fuzzy inference systems (ANFIS) were used for on-line classification and measurement of tool wear. The input vectors consist of selected features. For the on-line classification, the outputs are boring tool conditions, which are either usable or worn out. For the on-line measurement, the outputs are estimated value of the tool wear. Using BPN, five features were needed for the on-line classification of boring tools. They are the average longitudinal force, average value of the ratio between the tangential and radial forces, skewness of the longitudinal force, skewness of the tangential force, and kurtosis of the longitudinal force. Three features, the average longitudinal force, average of the ratio between the tangential and radial forces, and kurtosis of the longitudinal force, were needed for on-line measurement of tool wear. Using ANFIS, three features were needed for the on-line classification of boring tools. They are the average longitudinal force, average of the ratio between the tangential and radial forces, and kurtosis of the longitudinal force. Only one feature, kurtosis of the longitudinal force, was needed for the on-line measurement of tool wear using ANFIS. Both 5×20×1 BPN and 3×5 ANFIS can achieve a 100% success rate for the on-line classification of boring tool conditions. Using a 3×20×1 BPN for neural computing, the minimum flank wear estimation error is 0.29% while the minimum flank wear estimation error is 2.04% using a 1×5 ANFIS.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission

In modern manufacturing industry, developing automated tool condition monitoring system become more and more import in order to transform manufacturing systems from manually operated production machines to highly automated machining centres. This paper presents a nouvelle cutting tool wear assessment in high precision turning process using type-2 fuzzy uncertainty estimation on acoustic Emission. Without understanding the exact physics of the machining process, type-2 fuzzy logic system identifies acoustic emission signal during the process and its interval set of output assesses the uncertainty information in the signal. The experimental study shows that the development trend of uncertainty in acoustic emission signal corresponds to that of cutting tool wear. The estimation of uncertainties can be used for proving the conformance with specifications for products or auto-controlling of machine system, which has great meaning for continuously improvement in product quality, reliability and manufacturing efficiency in machining industry.     

基于图像连通区域数的刀具磨损状态特征提取实验研究

为了利用计算机视觉技术进行刀具状态监测,设计了机械加工刀具状态监测实验系统,并通过将图像处理技术引入到机械加工刀具磨损状态监测中,提出了一种通过提取工件表面图像的连通区域数来判断刀具磨损状态的新方法。该方法首先采集被加工工件的表面图像;然后对图像进行预处理,并对区域行程算法进行了改进,再用改进的区域行程标记算法对机械加工工件表面图像进行标记;最后通过统计连通区域数来判断刀具的磨损状态。理论和实验分析表明,由于加工工件表面图像的连通区域数和刀具磨损有很强的相关性,其可以间接判断刀具磨损情况,从而可达到对刀具状态进行监测的目的。实验表明,该方法计算简单、识别速度快,可以有效地判断刀具的磨损状态。     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

Advanced process monitoring—a major step towards adaptive control

Adaptive Control (AC) of machine tools requires many kinds of measured input data. The more information about the complex metal cutting process that can be obtained, the better the process can be controlled.

The paper describes an Adaptive Control Optimization (ACO) system for turning operations. The system continuously chooses Optimal Cutting Data (OCD), taking into account both economical criteria and technical limitations.

The system operates at three different levels:

• • Advanced Process Monitoring

• • Adaptive Control Constraint (ACC)

• • Adaptive Control Optimization (ACO).

Two commercial monitoring systems perform process monitoring. In addition, five independent measurement systems have been developed.

A dedicated vision system has been installed in the lathe to measure the tool flank wear between cuts. The flank wear data are utilized to predict the tool life. Based upon these predictions economical optimum cutting data can be calculated at the ACO level.

To obtain in-process real-time control of the metal cutting process the cutting forces are measured during machining. The forces are measured with conventional piezoelectric force transducers which are located between the turret housing and the cross-slide. The measured force signals are processed by a dedicated microcontroller at the ACC level and cutting data adjustments are fed back to the machine control.

A vibration measurement system, which either can be connected to an accelerometer or use the dynamic force signal from the piezoelectric force transducer, is part of a vibration control module at the ACC level. An ultra-fast signal processor performs the signal analysis.

The remaining two measurement systems—a high frequency tool signal analysis system and a power spectra analysis system—are mentioned in the paper but not further discussed.

Finally, the paper deals with how the strategies at the three different levels will be combined, in order to form an AC system. The monitoring tasks will always reside in the background and be activated if any failure occurs. The ACO subsystem will act as a path-finder and suggest cutting data. The active control tasks will, however, be carried out at the ACC level.     

Simulation study on the improvements of machining accuracy by using smart materials

This work utilizes smart material to counteract the radial disturbing cutting forces and reduce machining error in the turning process. The finite element method (FEM) is employed to explore the capability of such a method in controlling tool position. Toolpost dynamic response is investigated where the pulse width modulation (PWM) technique is launched for actuator voltage input. The result from tool response using dynamic absorber does not encourage the use of such a vibration attenuator in error elimination in the presence of the PWM voltage input. Even though increasing toolpost damping within a reasonable range shows a reduction in toolpost error, major improvement is noticed by modifying the PWM voltage level and its time duration. For error elimination, the estimate of static actuator voltage does not reflect the actual level of required dynamic applied voltage. This work also emphasizes the importance of tool bit to actuator stiffness and tool carrier (holder) to actuator stiffness in reducing tool positional error.     

基于随机模糊神经网络的刀具磨损量软测量技术

刀具磨损检测对于提高加工过程的自动化、高精度化、智能化具有重要意义．本文通 过检测电流信号基于随机模糊神经网络建立了刀具磨损量的软测量模型．该模型的创新之处 在于利用切削参数实时地调整网络的部分参数,从而可以减小切削参数与电流信号之间关系 对于刀具磨损估计的影响并且使得模型具有动态性、实时性．实验验证表明该方法是正确而 有效的．     

On-line tool wear monitoring in turning using neural networks

The on-line supervision of a tool's wear is the most difficult task in the context of tool monitoring. Based on an in-process acquisition of signals with multi-sensor systems, it is possible to estimate or classify wear parameters by means of neural networks. This article demonstrates that solutions can be improved significantly by using available secondary information about physical models of the cutting process and about the temporal development of wear. Process models describing the influence of process parameters are used for a dedicated pre-processing of the sensor signals. The essential signal behaviour in a certain time window is described by means of polynomial coefficients. These coefficients are used as inputs for feedforward networks considering the temporal development of wear (multilayer perceptrons with a sliding window technique and time-delay neural networks). With a combination of the proposed measures it is possible to obtain remarkable improvements of both tool wear estimation and classification.     

Hardware-in-the-loop simulator for stability study in orthogonal cutting

The self-excited vibrations due to the regenerative effect, commonly known as chatter, are one of the major problems in machining processes. They cause a reduction in the surface quality and in the lifetime of mechanical elements including cutting tools. Furthermore, the experimental investigations of chatter suppression techniques are difficult in a real machining environment, due to repeatability problems of hard to control parameters like tool wear or position dependent dynamic flexibility. In this work, a mechatronic hardware-in-the-loop (HIL) simulator based on a flexible structure is proposed for dimensionless study of chatter in orthogonal cutting. Such system reproduces experimentally, on a simple linear mechanical structure in the laboratory, any stability situation which can be used to test and optimise active control devices. For this purpose, a dimensionless formulation is adopted and the delay related to the phase lag of the actuator and the controller employed on the HIL is compensated.     

新型动磁式直线振荡执行器的动子位移自传感

针对一种新型无内定子动磁式直线振荡执行器,在建立其机电系统数学模型的基础上,提出一种基于全维状态观测器的动子位移自传感算法。通过对执行器输入电压和输出电流信号的处理和计算来估算动子位移。仿真和实验结果均表明:在变压变频控制方式下,该算法能实现不同电气驱动频率下的动子位移自传感;采用该算法进行行程估算的绝对误差最大值为0.32 mm,相对误差最大值为2.6%。此算法可以满足直线压缩机和直线泵类负载的变行程控制要求。     

一种新型谐振式非接触流体声发射传感器的研制

研制高灵敏度、安装使用方便、抗干扰能力强的传感器是刀具磨破损监测研究需要解决的关键技术 .本文根据刀具磨、破损监测的特点 ,研制了既可用于刀具磨损状态监测 ,也可用于刀具破损监测的谐振式高灵敏度流体声发射传感器 .对研制的流体声发射传感器性能进行了实验研究 ,结果表明传感器对刀具磨损产生的声发射信号具有较高的灵敏度 .     

Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i. e. , slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.     

移动质量激励悬臂梁振动主动控制系统

利用压电材料的正逆压电效应,实现了移动质量激励悬臂梁振动主动控制;建立了压电元传感方程和作动方程,进一步将其转化为状态空间模型中的状态方程和输出方程;设计了基于线性二次型最优控制(LQR)策略的振动主动控制器,以TMS320VC33 DSP芯片为核心组建了相应的硬件电路。实验结果表明:采用压电自感作动器可很好地抑制移动质量激励引起的悬臂梁振动。     

Wear State Recognition of Drills Based on/（-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.     

基于计算智能算法的铣刀状态监测

本文提出了基于智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种计算智能数据融合技术-小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,本文提出的几种计算智能数据融合技术均能够有效地完成刀具磨损量预测。     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

Cutting tool wear estimation for turning

The experimental investigation on cutting tool wear and a model for tool wear estimation is reported in this paper. The changes in the values of cutting forces, vibrations and acoustic emissions with cutting tool wear are recoded and analyzed. On the basis of experimental results a model is developed for tool wear estimation in turning operations using Adaptive Neuro fuzzy Inference system (ANFIS). Acoustic emission (Ring down count), vibrations (acceleration) and cutting forces along with time have been used to formulate model. This model is capable of estimating the wear rate of the cutting tool. The wear estimation results obtained by the model are compared with the practical results and are presented. The model performed quite satisfactory results with the actual and predicted tool wear values. The model can also be used for estimating tool wear on-line but the accuracy of the model depends upon the proper training and section of data points.