刀具破损状态声发射信号的多分辨率分析

刀具状态监控对保障生产安全和产品质量具有重要意义。采用声发射(AE)传感器来采集切削过程中发出的AE信号,采用多分辨率分忻法对正常切削、刀具破损、断屑时发出的AE信号进行分析,并提取出反映刀具破损状态的特征量;最后采用BP神经网络实现了刀具破损状态的自动识别。     

基于云模型和稀疏贝叶斯技术的刀具磨损状态识别技术研究

针对刀具磨损状态监测和磨损量预测研究中特征提取这一关键技术,提出采用声发射传感器和功率传感器采集机床刀具磨损相关的信号信息,采用两种信号采集的方法可以避免单一信号本身自有的缺陷。采用云模型算法能够科学地耦合两种信息,并提取信号中反映刀具磨损量的特征因素;使用稀疏贝叶斯方法建立模型进而预测刀具磨损量,实现了对刀具磨损的监控,提高了刀具磨损监控的效率和准确性。     

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。     

重型车刀破损声发射监控装置

根据声发射信号特征,用声发射信号判断刀具破损,开发研制出“重型切削刀破损声发射监控装置。”此装置用８０９８芯片为微处理器进行振铃计数、声发射信号实时分析处理。当刀具破损或严重磨损时,监控装置发出声光报警信号,并使车床横向退刀。信号响应时间小于１ｓ,其报警成功率达９０％。     

基于小波分析的刀具破损智能监控系统

提出了利用检测进给电机电流对钻削加工过程中的刀具破损进行在线监控的系统。该系统采用离散小波分析技术处理电机电流信号,有效提取刀具破损时的特征。探讨中断型宏指令功能在刀具破损在线监控系统中的应用。利用该监控系统和中断型宏指令,能够实时地识别加工过程刀具的破损,并及时报警、自动换刀等,大大减少了机床的故障停机时间,提高利用率。     

刀具破损的传感与预测

由于对生产过程自动化系统化的要求日渐增长,刀具破损的传感和预测技术已成为一个关键问题。本文评述了刀具破损的预测和传感技术的现状,对各种传感信号如力、温度、功率、振动、声音和表面粗糙度等,特别是声发射传感进行了仔细分析,概括了各自的特点和存在的问题,列举了一些实例。最后指出,必须研制简单可靠的传感器和传感技术,继续努力寻求新的刀具损坏的预告和传感的可能性,才能使这一技术达到实用化。     

信息

TC-S2A钻攻中心具有刀检功能，避免因刀具破损引起的工件报废、刀具或机床的损坏。它采用气缸推动刀检传感器去接触刀具，以便检测刀具是否破损，如已破损，产生报警，机床停止。本文详述如何使用机床刀检功能的过程。     

精密数控机床铣刀故障监测方法的研究

刀具在加工过程中不可避免的存在着磨损和破损现象,刀具的消耗直接导致工件精度下降和生产成本增加。开展了一系列实验,深入研究刀具状态监测方法,构建了新型铣削过程刀具磨损监测试验系统。通过振动传感器和声发射传感器对铣削过程中不同磨损程度刀具的信号进行检测、采集、分析。选择对刀具磨损状态反映敏感的特征量。采用BP神经网络,建立刀具磨损特征向量与刀具磨损状态之间的非线性映射关系。     

基于切削功率的数控车削批量加工刀具磨损在线监测

为了实现数控车削批量加工刀具磨损状态的在线监测,在分析切削功率与刀具磨损量关系的基础上,考虑加工参数对切削功率的影响,基于正交实验设计与响应面法,建立了切削功率与刀具磨损量及加工参数之间的回归模型。提出一种实时更新切削功率阈值的刀具磨损状态在线监测方法。该方法首先对功率信号进行滤波处理,结合数控系统判断机床的运行状态,然后实时计算切削功率阈值并与实际加工过程切削功率进行比较来监测刀具的磨损状况。通过实验案例自动在线监测数控车削过程中刀具磨损的情况,验证了该方法的有效性。     

刀具的过载保护与自动调整

金属切削机床的过载保护是保护电动机的过载,在有些机床中,还要对加工的刀具进行保护,如刀具铣床。多数刀具过载破损时,电机并没有过载。刀具损坏不仅使成本提高,效率降低,严重时还会使机床的部件损坏,使机床的精度严重下降。因此,刀具过载保护对于这类机床显得十分必要。 这类机床有以下特点: 1.不同规格的刀具所允许的切削载荷不同,多数刀具在破损时都达不到主电机的额定功率。 2.同型号机床,由于出厂日期不同,机床的传动效率也不同。所以,同品种同模数刀具在不同机床上加工,稳定切削时的主电机功率就不同,有的差别很大。 3.由于刀具的材…     

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.     

声发射预测刀具破损的研究

介绍一种用声发射（ＡＥ）进行刀具破损预测的方法。试验结果证明，刀具破损前会出现预兆性的ＡＥ信号，并可把该信号从背景噪声中检测出来。该方法使用的检测系统具有成本低、能耗低和结构简单的特点。     

Acoustic emission method for tool condition monitoring based on wavelet analysis

It is believed that the acoustic emission (AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection. However, AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems and it is difficult to obtain an effective result by these raw acoustic emission data. In this article, a technique based on AE signal wavelet analysis is proposed for tool condition monitoring. The local characterize of frequency band, which contains the main energy of AE signals, is depicted by the wavelet multi-resolution analysis, and the singularity of the signal is represented by wavelet resolution coefficient norm. The feasibility for tool condition monitoring is demonstrated by the various cutting conditions in turning experiments.     

Tool State Detection by Harmonic Wavelet and Sample Entropy

It is a fact that acoustic emission(AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection.However,AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems,it is difficult to obtain a reliable result by these raw AE data.It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise.The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman(2000) sample entropy for detecting weak singularity signals embedded in strong signals.First,the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet,at the same time,the contours of the mesh map with log space is induced.Second,the profile map of the three-dimensional time-frequency mesh map is offered,which corresponds to decomposed level on harmonic wavelets.Final,by computing sample entropy in each level,the weak singularity signal can be easily extracted from strong noise.Machining test was carried out on HL-32 NC turning center.This lathe does not have a tailstock.Tungsten carbide finishing tool was used to turn free machining mild steel.The work material was chosen for ease of machining,allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments,the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool,54 group data are sampled by AE.The sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed,wear tool and shaper tool can be distinguished obviously by the sample entropy value at the 12th level,this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring.     

加工过程刀具破损的声发射传感监测新技术

主要介绍一种适合加工过程刀具破损的声发射传感监测新技术,论述了新型传感器的结构、工作原理及信号处理方法。同时还介绍了适合自动化加工过程的新型刀具破损实时监控系统及其应用效果。     

刀具磨损、破损的声发射监控法的研究

本文讨论了声发射监控法在线监控刀具磨损、破损的原理及实现方法。描述了声发射信号的产生及其特征以及系统硬件原理图。实验及结果证明了此方法的可行性。     

基于独立分量分析的切削声发射源信号分离

针对切削声发射(Acoustic Emission,AE)信号的多目标状态源并行分离问题和同频干扰源分离问题,引入独立分量分析(Independent Component Analysis,ICA)技术作为研究工具,用刀具破损、切屑折断和环境噪声三个AE源的线性混合模拟切削AE信号,尝试用FastICA算法分离目标状态...     

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.     

加工过程刀具破损监测的声发射传感新技术

主要介绍一种适合加工过程刀具破损监测的声发射传感新技术,论述了新型传感器的结构、工作原理及信号处理方法。同时还介绍了适合自动化加工过程的新型刀具破损实时监控系统及其应用效果。     

Feature extraction of AE characteristics in offshore structure model using Hilbert-Huang transform

This paper addresses an application of recently developed signal processing tool based on the Hilbert-Huang transform (HHT) to characterize the acoustic emission (AE) signals released from the offshore structure model. The AE signals from the cracks in the welded steel nodes of the offshore structure model are collected during the tensile testing in water and the simulated AE signals are also collected in the offshore structure model. Instantaneous frequency and energy features based on local properties of the AE signals are then extracted using the Hilbert-Huang transform method. In order to demonstrate the advantage of the AE feature extraction techniques based on Hilbert-Huang transform, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the method based on HHT better characterizes the AE signals than the classical AE techniques. It can be concluded that the AE signal analysis based on HHT is an effective tool to extract the features and this opens perspectives for crack recognition in offshore structures.