基于小波分析的数控刀具状态在线监控系统

提出了一种利用检测进给电机电流实现切削加工过程中刀具破损的在线监控系统.在该系统中,离散小波分析技术被用来实现对电机电流信号的处理,并有效地提取了刀具破损时的特征;探讨了中断型宏指令功能在刀具破损在线监控系统中的应用;经实践证明,利用该监测系统和中断型宏指令,能够实时的识别加工过程刀具的破损,并能及时报警、自动换刀等,机床的故障停机时间大大减少,利用率得到了提高.     

中断型宏指令功能在刀具破损在线监控系统中的应用

介绍了FANUC系统的中断型宏指令功能;结合应用实例,讨论了中断型宏指令功能在刀具破损在线监控系统中的应用。     

基于数控系统的刀具失效实时响应研究

目前刀具在线监控系统通常需要开发专门的控制软件来实现刀具损坏的实时响应。这对于普通的数控机床用户来说比较复杂、难以掌握,使用和维护起来也很困难。本文基于FANUC数控系统,利用中断型宏指令功能和系统变量,通过编制数控程序来实现在线监控系统检出刀具实效后的相关处理,能满足一般实时性要求,且灵活性好、应用方便、易于掌握,有利于刀具在线监控技术在数控机床上的应用推广。     

深孔滚镗加工刀具状态在线监控系统

本文分析了深孔滚镗加工过程中的主电机和进给电机的功率信号特征，建立了深孔加工刀具状态在线监控系统。该系统可以有效地识别深孔滚镗加工过程中的镗刀磨损、破损及机床的“堵车”现象。     

用声发射和电机电流检测技术实现刀具破损的监测

采用声发射（ＡＥ）和电机电流多特征参数融合检测的方法,研制了具有独自特点的刀具破损监测系统。介绍了系统的软硬件结构,建立了实现参数检测的数学模型,并用实验证明了该系统在线监测刀具破损的可行性     

基于虚拟仪器的FMS运行状态监测系统

针对FMS的零件加工过程,基于刀具的破损机理,分析了刀具小面积破损及刀具大面积破损情况下切削力及电机电流的变化规律,并建立了加工状态监测的实验系统.基于神经网络理论,建立了BP网络的加工状态识别系统理论模型.实验证明,该监测方案及建立的理论模型是正确的.     

高校科技成果

用途:适用于数控机床、加工中心以及FMS加工过程中刀具破损的在线监控,预防事故发生,提高加工效率。特点:①在加工过程中同时监测AE(声发射)信号和主轴电机电流,综合判断刀具(车刀、钻头、     

数控切削中刀具在线监控系统的研究与应用

本文提出了一种数控加工过程中刀具在线监控系统,并详细介绍了用主轴电机电流作为刀具在线监控系统信号源的原理及优势。该系统的应用可使数控加工中机床故障停机时间减少,工艺稳定性、机床利用率、刀具寿命和生产管理追溯性等性能均有质的提升。     

基于FANUC FOCAS的大型精密工件在线测量与监控系统设计

针对大中型精密工件在自动线加工过程中,存在对位搬运困难,加工精度要求高,出现加工缺陷时修复困难、损失大等问题,利用Renishaw的OMP60测头和对刀仪,结合FANUC的FOCAS软件库,通过C#编写机床实时采集和监控软件,配合数控系统G代码和宏指令,构建一套在线位置找正、尺寸测量和刀具监控系统,实现工件的对位位置的自动找正和刀具破损的在线监控,保证了工件的加工质量,满足了自动线上大中型精密工件的自动加工需求。     

加工中心刀具破损监控系统的研制

综合应用声发射（AE）和电机电压电流信号法监控刀具破损，可提高刀具破损检出率。以此开发的加工中心刀具破损监控系统，对1mm以上的钻头及3mm以上的铣刀破损检出率均在98％以上。     

Real-time detection of the breakage of small diameter drills with wavelet transform

This paper presents a real-time tool breakage detection method for small diameter drills using acoustic emission (AE) and current signals. Using the transmitted properties of the AE signal, apparatus for detecting the AE signal for tool breakage monitoring was developed for a machine centre. The features of tool breakage were obtained from the AE signal using typical signal processing methods. The continuous wavelet transform (CWT) and the discrete wavelet transform (DWT) were used to decompose the spindle current signal and the feed current signal, respectively. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent real-time capability and a high success rate for the detection of the breakage of small diameter drills using combined AE and current signals.     

A microcontroller-based end milling cutter monitoring and management system

The monitoring of end milling cutting operations for tool breakage is achieved using a low-cost microcontroller-based system. The system is based upon acquiring and analysing machine tool-based signals for characteristic responses to tool breakage. Spindle speed and load signals are shown to be responsive to tool condition and thus capable of supporting the deployed approach. The resulting system operates in real time with tool breakage detection consistently diagnosed within two revolutions. The monitoring function is extended to consider tool wear using analysis methods applied in the time and frequency domains. Decisions about tool condition are made by integrating all relevant information into a rule base. Higher-level tool management functions supported by the deployed system are identified.     

Tool breakage detection in CNC high-speed milling based in feed-motor current signals

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the I _rms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement.     

基于人工神经网络的变切削条件下钻头磨损监控

基于人工神经网络变切削条件下的钻头磨损监控系统，以机床主轴和进给轴的电机功率（电流）信号的为监控信号，并通过机床的速度向量识别机床的加工状态；通用对监控信号的提取和预处理，得到人工神经网络模型的输入（有效切削功率和切削用量）用３层ＢＰ网络对钻头的磨损量进行预报。     

Motor current prediction of a machine tool feed drive using a component-based simulation model

In recent times, simulation techniques have been rapidly accepted by the machine tool industry. However, most existing simulation studies have focused on a particular machine tool and described an entire machine tool feed drive as a single combined system. This paper presents a method to accurately predict motor current (torque) behavior and acquire a more generalized and accurate dynamic simulation model for a machine tool feed drive. To improve the generality, a component-based approach is introduced. In this approach, the feed drive model is composed of subcomponent models, and each component mechanism is then independently modeled. In the developed model structure, the parameters of the subcomponent model can easily be determined by using product datasheets or simple parameter identification based on motor current measurements. To enhance the model accuracy in predicting the motor current, an improved friction model including time-dependent frictional characteristics and rolling contact conditions was introduced to the simulation. The performance of the developed dynamic simulation model is demonstrated through a comparison with real machine tool behavior.     

Prediction of tool breakage in face milling using support vector machine

A new approach is proposed using a support vector machine (SVM) to classify the feature of the cutting force signal for the prediction of tool breakage in face milling. The cutting force signal is compressed by averaging the cutting force signals per tooth to extract the feature of the cutting force signal due to tool breakage. With the SVM learning process, the output of SVM’s decision function can be utilized to identify a milling cutter with or without tool breakage. Experimental results are presented to verify the feasibility of this tool breakage prediction system in milling operations.     

Breakage detection of small-diameter tap using vision system in high-speed tapping machine with open architecture controller

In this research, a vision system for detecting breakages of small-diameter taps, which are rarely detected by the indirect in-process monitoring methods such as acoustic emission, cutting torque and motor current, was developed. Two HMI (Human Machine Interface) programs to embed the developed vision system into a Siemens open architecture controller, 840D, were developed. They are placed in sub-windows of the main window of the 840D and can be activated or deactivated either by a softkey on the operating panel or the M code in the NC part program. In the event that any type of tool breakage is detected, the HMI program issues a command for an automatic tool change or sends an alarm signal to the NC kernel. An evaluation test in a high-speed tapping machine showed that the developed vision system was successful in detecting breakages of small-diameter taps up to M1.