基于声信号小波分析的刀具破损监测

提出了基于切削声信号的刀具破损监测方法。通过对破损声信号进行小波分析,提取出了与刀具破损具有相应关系的特征频带,去除了冲击声信号、刀具切入声信号等与刀具破损具有相似特征的声信号干扰,通过设定合适的阈值,能够较好地监测刀具的破损。这种监测方法为刀具的破损监测提供了一种新的途径。     

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BP神经网络的微孔钻削实时监测

为防止微孔钻削过程中钻头折断,研制微孔钻削在线监测系统。该系统以主轴电机三相电流对应的电压信号为监测对象,应用神经网络建立钻头磨损状态与信号特征的关系模型,以此获取隐含微细钻头磨损状态的信息值。实验结果表明,应用此系统进行微孔钻削在线监测,可以有效避免微钻头的折断,提高钻头的利用率。     

基于声发射的铣刀破损信号特征的提取与优化

铣刀破损监测对实现加工自动化具有重要的意义.提出了基于小波变换的铣刀声发射破损特征提取与优化方法.首先,采用小波变换对铣刀声发射信号进行多分辨率分解,然后提取各频段子信号的能量比作为刀具破损监测的特征量.通过对正常切削、随机冲击和刀具破损这三类信号的比较分析,证明了该特征提取方法能够有效地反映刀具状态.最后,通过正交统计,分析了切削速度、进给速度和切削深度对特征量的影响,并对特征量进行优化.     

GH901高温合金盘件复杂区域微小缺陷超声相控阵检测信号的小波降噪研究

针对GH901高温合金盘件复杂区域微小缺陷的相控阵超声检测技术,分析了检测信号,采用一种随分解尺度变化的阈值设置准则对检测信号进行小波降噪处理以提高信噪比。结果表明,该方法可在较好保留盘件微小缺陷信号的同时滤除噪声信号,有效的提高了相控阵超声检测信号信噪比,对于提高GH901高温合金盘件复杂区域微小缺陷的相控阵超声检测能力具有重要作用。     

基于声发射和振动信号的振动钻削钻头故障诊断试验研究

针对钻削过程中钻头状态监测问题,基于声发射采集系统和振动采集系统设计超声轴向振动钻削钻头故障监测装置,分别应用完整钻头和故障钻头进行45钢板的超声振动钻削对比试验,采集不同钻头状态的AE和振动信号,通过时域分析、频域分析和小波分解,分析故障钻头对AE和振动信号的影响。试验结果表明：通过AE和振动信号判别钻头状态,判别结果与实际一致,能够实现钻头的故障诊断。     

用声发射检测钻头破损(下)

5.总体构成 现在,把已开发的声发射钻头破损检测器装在机床上而构成的系统如图13所示。当切削加工中发生钻头破损时,用安装在工作台上的声发射传感器来检测钻头破损所发出的信号,并在模拟信号处理部处理其信号,该系统具有输出刀具破损信号的功能。例如:使用此装置时,由其输出来控制ATC(刀具自动交换装置),可以自动交换损坏     

微小孔成孔监测技术

微小孔机械钻削时不可避免地出现微钻头弯曲、折断现象,造成钻头和辅助工时的严重浪费。为解决这一问题,有必要对微孔加工钻削过程进行在线实时监测。介绍目前广泛采用的刀具状态在线自动监测技术,并对微小孔成孔监测技术的未来发展趋势进行初步探讨。     

数控机床刀具磨损监测数据处理方法

数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.文章回顾了国内外各种分析刀具磨损信号处理方法的研究工作,详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术等四种数据处理方法,并简单展望了数据处理方法未来发展趋势.     

基于HHT变换的声发射源平面定位方法

为了应对空间碎片的威胁,研制了一种基于声发射技术的用于实时监测空间碎片撞击航天器的在轨感知系统。对平面声发射源精确定位技术提出了需求。声发射信号属于非平稳随机信号,传统的小波变换无法充分获得其中携带的信息。利用HHT技术分析声发射信号波形,改进了AO模态到达时刻的确定算法,提高了线定位精度。在此基础上,将平面定位问题转化为求取函数最小值的优化问题,并利用单纯形法进行求解。在铝合金板上对铅芯折断波源进行了定位试验,结果表明,相对于小波变换,HHT更适于分析声发射信号;改进后的线定位方法和双时标法可有效应用于各向同性板的定位问题。研究结果为空间碎片在轨感知系统的研制提供了参考。     

The shape characteristic detection of tool breakage in milling operations

Detection of tool failure is very important in automated manufacturing. All previously developed tool breakage detection approaches in milling operations have adopted the strategy of parameter detection in which the detection of tool breakage was carried out according to values of specific parameters selected to reflect tool state (with or without tool breakage). In this paper the new concept of shape characteristic detection of tool breakage in milling operations is proposed. The detection of tool breakage is conducted according to the shape characteristics of discrete dyadic wavelet decomposition of cutting force. By means of the proposed method, the influence caused by the variation of cutting parameters and transients is eliminated. The proposed method is conducted in two steps. In the first step, cutting force signals are decomposed by discrete dyadic wavelet, with the shape characteristic vectors then being generated by the proposed shape characteristic vector-generating algorithm. In the second step, the shape characteristic vectors are fast classified by the ART2 neural networks. The accuracy and effectiveness of the proposed method are verified by numerous experiments.     

STUDY ON THE EFFECTS OF BREAKAGE OF SINGLE FIBER ON CREEP BEHAVIOR OF FIBER REINFORCED COMPOSITES

A 3-D micro cell model with multi-fibers has been presented to study the effects of breakage of single fiber on the whole creep behavior of fiber reinforced composites by finite element method (FEM). Before the fiber breakage, the stresses of all fibers are identical. With the creep time increasing, stress in fiber increases but stress in matrix decreases. It is assumed that the fiber breakage occurs when the stress in fiber reaches a critical value. The stress redistribution resulted from the breakage of fiber has been obtained. The influence on the axial stress of the broken fiber is local. The stress in the all fiber sections is not uniform. There is a local stress concentration in the matrix. And this stress concentration in the matrix is more and more serious with the creep deformation. The stress transference of the loading due to the fiber breakage has been studies numerically. It is found that the fibers near to the broken fiber will take over more loading.     

用热时效法解决轧钢的断辊问题

洛阳钢铁公司开坯车间７５ＣｒＭｏ铸钢轧辊极易断裂，采用自然时效，存在周期长，且效果不甚理想等弊端，使用热时效（低温回火）法后，彻底解决了断辊问题。     

Development of a tool failure detection system using multi-sensors

Tool monitoring and machine diagnosis in real machining have been crucial to the realization of fully automated machining. Also, the on-line detection technique of the tool breakage in machining should be supported. The effect of tool breakage is usually revealed from an abrupt change in the processed measurements, which is in excess of a threshold value. Although these techniques are generally effective for a specific cutting condition, they are often not sufficiently reliable for use in production due to the inability of single measurement to reflect tool breakage under various cutting conditions. In order to enhance the reliability of tool breakage signatures obtained from a single sensor, an integrated approach based on measurements from several sensors has been put forward. In this study, the tool breakage detection method using multi-sensors is proposed and the sensor fusion algorithm is developed to integrate and make decisions from data measured through the multisensors. Also, the performances of this scheme are compared and evaluated with real cutting process.     

Modeling fracture in carbon nanotubes using a meshless atomic-scale finite-element method

A meshless atomic-scale computational method was developed by taking account of structural dynamic evolution, such as atomic bond breakage and regeneration. This method, based on energy minimization, is an extension of B. Liu et al.’s atomic-scale finite element method (AFEM). The proposed method is faster than the standard conjugate gradient method and AFEM and can thus significantly save computational time especially in studying large-scale problems. The bond breakage of single-wall carbon nanotubes was studied.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

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.