Driver current analysis for sensorless tool breakage monitoring of CNC milling machines

Tool wear and breakage detection is one of the most important problems found during manufacture in automated CNC machines. From several techniques devoted to sense tool condition, driver current monitoring has been used for a sensorless approach. In order to efficiently use the driver current monitoring technique an exhaustive analysis on the nature of the real components of the signal is required. The novelty of this paper is to present a driver current signal analysis to estimate the influence of the most important spurious signal components in order to determine the optimal parameters for signal conditioning. Beside the cutting force signal, the spurious signals considered in the analysis are high-frequency noise, current control commutation and ball screw effects. The analysis is compared with experimental data in order to validate the model and a case study is presented to show the general procedure.     

An overview of approaches to end milling tool monitoring

The increase in awareness regarding the need to optimise manufacturing process efficiency has led to a great deal of research aimed at machine tool condition monitoring. This paper considers the application of condition monitoring techniques to the detection of cutting tool wear and breakage during the milling process. Established approaches to the problem are considered and their application to the next generation of monitoring systems is discussed. Two approaches are identified as being key to the industrial application of operational tool monitoring systems.Multiple sensor systems, which use a wide range of sensors with an increasing level of intelligence, are seen as providing long-term benefits, particularly in the field of tool wear monitoring. Such systems are being developed by a number of researchers in this area. The second approach integrates the control signals used by the machine controller into a process monitoring system which is capable of detecting tool breakage. Initial findings mainly under laboratory conditions, indicate that both these approaches can be of major benefit. It is finally argued that a combination of these approaches will ultimately lead to robust systems which can operate in an industrial environment.     

A cutting power model for tool wear monitoring in milling

This paper describes a cutting power model in face milling operation, where cutting conditions and average tool flank wear are taken into account. The cutting power model is verified with experiments. It is shown with the simulations and experiments that the simulated power signals predict the mean cutting power better than the instantaneous cutting power. Finally, the cutting power model is used in a cutting power threshold updating strategy for tool wear monitoring which has been carried out successfully in milling operations under variable cutting conditions.     

Real-time tool wear monitoring in milling using a cutting condition independent method

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.     

Tool breakage detection using support vector machine learning in a milling process

In this paper, an intelligent tool breakage detection system which uses a support vector machine (SVM) learning algorithm is proposed to provide the ability to recognize process abnormalities and initiate corrective action during a manufacturing process, specifically in a milling process. The system utilizes multiple sensors to record cutting forces and power consumptions. Attention is focused on training the proposed system for performance improvement and detecting tool breakage. Performance of the developed system is compared to the results from an alternative detection system based on a multiple linear regression model. It is expected that the proposed system will reduce machine downtime, which in turn will lead to reduced production costs and increased customer satisfaction.     

TWEM, a method based on cutting forces—monitoring tool wear in face milling

One of the most important objectives in manufacturing is the intelligent machining system. To come to such a solution, the tool wear has to be determined on-line during the cutting process on unmanned machining systems. This contribution discusses the results experimentally obtained in face milling with a new rotating dynamometer. The paper introduces the concept of tool wear indicators which can be determined by simple analysis of the feature parameters of cutting force signals. The disturbance of the cutting force signals obtained by using the rotating dynamometer can be solved by applying tool wear indicators such as Normalized Cutting Force indicator (NCF) and Torque-Force Distance indicator (TFD). The Method for Tool Wear Estimation—TWEM is proposed.     

数控机床加工曲面时刀具破损的小波分析

介绍小波变换思想及特点，根据傅里叶变换原理，结合刀具破损信号，分析了快速小波变换－Mallat算法。实验表明多分辨分析的方法，对于刀具破损突变信号具有精确时－频定位和易于监测的优点，能够有效处理刀具破损监控的信号。     

基于数字信号处理器的高频逆变电阻点焊电源设计与实验

研究了基于数字信号处理器(DSP)的高频逆变电阻点焊电源,重点介绍逆变电路的工作原理以及控制方法,并通过实验验证了点焊电源的稳定性和可靠性。结果表明,基于数字信号处理器的高频逆变电阻点焊电源采用全桥逆变输出、PI调节和三段焊输出电流,实现了电路软开关,提高了焊机逆变频率,保证了焊接过程的稳定性和焊件的点焊质量。     

Complexity measure of motor current signals for tool flute breakage detection in end milling

Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors.     

Real time implementation of on-line tool condition monitoring in turning

This paper describes a real-time tool condition monitoring system for turning operations. The system uses a combination of static and dynamic neural networks with off-line and on-line training and cutting force components are used as diagnostic signals. The system is capable of monitoring several wear components simultaneously. The wear estimation system has been implemented experimentally to evaluate its suitability for use in shop floor conditions. The tests were performed in real time with different cutting conditions. The experimental results showed that the system was successful in predicting three wear components in real time. However, the accuracy of the wear prediction was not the same for all three wear components. The crater wear predictions were less accurate partly because of the opposing effects of crater and flank wear components on cutting force components.     

基于DSP的全数字进给驱动系统研究

介绍了一种基于DSP的无位置传感器无刷直流电动机全数字数控系统。该系统充分利用了DSP端口资源丰富，运算速度快的特点，使系统结构简单、有效。实验表明系统获得了良好的控制性能。     

Micro-end-milling-III. Wear estimation and tool breakage detection using acoustic emission signals

Acoustic Emission (AE) signals have been used to monitor tool condition in conventional machining operations. In this paper, new procedures are proposed to detect tool breakage and to estimate tool condition (wear) by using AE. The proposed procedure filters the AE signals with a narrow band-width, band-pass filter and obtains the upper envelope of the harmonic signal by using analog hardware. The envelope is digitized, encoded and classified to monitor the machining operation. The characteristics of the envelope of the AE were evaluated to detect tool breakage. The encoded parameters of the envelope of the AE signals were classified by using the Adaptive Resonance Theory (ART2) and Abductory Induction Mechanism (AIM) to estimate wear. The proposed tool breakage and wear estimation techniques were tested on the experimental data. Both methods were found to be acceptable. However, the reliability of the tool breakage detection system was higher than the wear estimation method.     

基于DSP320F2812的数字化CO2焊机研制

设计了数字化CO2焊机的硬件结构,并根据CO2焊接工艺的特点,编写了相应的控制软件,实现了整个焊接系统的数字化.试验结果表明,基于DSP320F2812芯片控制的焊接电源具有良好的动特性,获得了较好的控制效果,为控制策略的智能化和最优化提供了良好的平台.     

机床主轴进给驱动系统新型脉宽调制方法研究

文章提出了一种可用于数控机床主轴进给驱动系统的新型空间矢量PWM(SVPWM)控制方法,该方法实现简单、实用性强,在保留了常规SVPWM的优点基础上,通过改变注入零矢量时间的方法,可进一步降低调速系统功率器件的开关频率和开关损耗.文章给出了这种SVPWM方法的实现思路,并结合DSP进行了实验研究,实验结果证明了该方法的有效性.     

On-line monitoring of tool wear in turning using a neural network

Tool wear has long been identified as the most undesirable characteristic of the machining operations. Flank wear, in particular directly affects the workpiece dimensions and the surface quality. A reliable and sensitive technique for monitoring the tool wear without interrupting the process, is crucial in realization of the modern manufacturing concepts like unmanned machining centres, adaptive control optimization, etc. In this work an optoelectronic sensor is used in conjunction with a multilayered Neural Network for predicting the flank wear on the cutting tool. The gap sensing system consists of a bifurcated optical fibre, a laser source and a photodiode circuit. The output of the photodiode circuit is amplified and converted to the digital form using an A/D converter. The digitized sensor signal along with the cutting parameters form the inputs to a three layered, feed forward, fully connected Neural Network. The Neural Network, trained off-line using a backpropagation algorithm and the experimental data, is used to predict the flank wear. A geometrical relation is also used to correlate the flank wear on the cutting tool with the change in the workpiece dimension. The values predicted using the Neural Network and those calculated using the geometrical relation are compared with the actual values measured using a tool maker's microscope. Results showed the ability of the Neural Network to accurately predict the flank wear.     

Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II)

Optimization of cutting parameters is important to achieving high quality in the machining process, especially where more complex multiple performance optimization is required. The present investigation focuses on the multiple performance optimization on machining characteristics of glass fiber reinforced plastic (GFRP) composites. The cutting parameters used for the experiments, which were carried out according to Taguchi’s L₂₇, 3-level orthogonal array, were cutting speed, feed and depth of cut. Statistical models based on second-order polynomial equations were developed for the different responses. The Non-dominated Sorting Genetic Algorithm (NSGA-II) tool was used to optimize the cutting conditions, yielding a non-dominated solution set that is reported here.