基于无线传感器网络的分布式监测系统

在超精密加工过程中,一个有效的监测系统能够保持加工刀具处于最佳的加工条件,延长刀具寿命,提高零件加工质量.该文提出一种基于无线传感器网络的分布式监测系统,实现了多机床的实时监测.实验结果显示了该系统的有效性.     

高速铣削颤振识别和稳定性预测

颤振是刀具与工件之间剧烈的自激振动,是影响工件表面质量与刀具磨损的重要因素。通过高速铣削试验,对加工过程中铣削力与振动信号进行分析,给出了一种通过监测加工过程中信号功率谱能量比变化来识别颤振的方法。试验结果表明:颤振发生时信号功率谱最主要的特性是在主轴转动频率、切削频率及其谐波两边等间距处会出现相应的颤振频率,当主颤振频率处的能量超过一定的阈值时,加工系统颤振,否则,无颤振。建立了颤振动力学模型,通过试验获得了铣削系统频响函数和铣削力系数,绘制了铣削加工稳定性曲线。结合提出的颤振识别方法,验证了动力学模型的准确性,可为实际加工中合理选择加工参数和颤振监测提供参考。     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

钛合金铣削温度的有限元仿真研究

针对钛合金铣削加工中刀具磨损严重,加工效率低等问题,应用专用金属切削有限元仿真软件Advant Edge FEM,对钛合金TC4的铣削加工过程进行二维切削仿真。研究切削过程中切削温度的分布情况,及刀-屑接触区温度随切削速度变化的规律,为提高刀具寿命以及切削参数的优化提供一定的参考依据。     

CATIA2.5轴平面铣削方法

平面铣削(Facing)是在水平切削层上创建刀位轨迹,用来去除工件上的材料余量的一种加工方法.平面铣削的刀具轴线是垂直于切削层平面,它常用于直壁的平面零件加工.平面铣削是2.5轴加工,虽然简单,但在整个零件的加工过程中有较为广泛的应用.下面就简要介绍平面铣削的过程和方法.     

加工过程的模糊自适应PID控制

经典的加工过程模型随切削深度、主轴转速、加工材料、刀具形状和磨损程度不同而不同,因而具有时变性。实际加工过程要比理论上推导出的模型复杂得多,是一种具有非线性、时变性和影响因素不确定的复杂系统,甚至难以用合适的数学模型来表示。结合PID和模糊控制两者的优点,建立一种加工过程的模糊自适应PID的控制方法。对模糊自适应PID控制算法进行了理论分析,基于Matlab建立了铣削加工过程的仿真模型。仿真结果表明,运用模糊自适应PID控制方法,系统的调节时间缩短,响应速度加快,抗干扰能力和适应参数变化的能力要优于增益自适应的PID控制。     

虚拟切削加工系统的设计与实现

虚拟制造是近年出现的一种先进制造技术,虚拟切削加工在虚拟制造中占有重要地位。为了提供虚拟的加工环境和验证工艺设计的正确性,对切削加工过程的计算机仿真方法进行了研究,以OSG作为图形支持系统,用VC++开发了切削加工仿真系统。该系统实现了对毛坯切割的仿真,可对刀具运动切割情况进行实时监控,较好的再现了加工中毛坯的成型过程。     

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。     

微径球头铣刀铣削表面误差建模与仿真

以微细铣削表面误差为研究对象,考虑尺度效应,建立了微径球头铣刀铣削力模型.将刀具简化为阶梯状悬臂梁,运用虚位移原理结合机床-工件系统变形获得刀具在铣削力作用下的变形量,并将其耦合到切削刃轨迹中,最终建立了综合主轴径向跳动、最小切削厚度、刀具弹性变形及机床-工件系统变形等因素的铣削过程表面创成物理模型.在此基础上,提出了微径球头铣刀铣削表面误差三维仿真算法,并通过仿真算例分析了各因素对工件铣削表面误差的影响.最后通过实验验证了该算法的有效性和可行性.     

基于Web的铣削过程颤振稳定域远程仿真系统研究

文章在分析数控加工铣削过程颤振稳定域仿真技术,介绍MATLABWebServer应用程序体系结构与开发原理的基础上,研究开发了基于Web的数控加工铣削过程颤振稳定域远程仿真系统;该系统实现了MATLAB语言与HTML语言的结合应用,为数控加工过程切削参数的合理、有效地选择提供了一种网络化远程仿真工具和方法,整个系统的主要功能在实际数控加工中得到了相应的验证。     

基于小波神经网络监测刀具状态的研究

针对切削过程中振动信号和AE信号的特点，提出一种基于小波分析和BP神经网络的刀具磨损监测系统。该系统能融合振动和AE信号的特征，描述信号特征与刀具状态的非线性关系，以此识别刀具状态。试验表明基于小波神经网络的刀具磨损状态监剩系统是有效的。     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

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.     

基于多传感器的刀具状态模糊识别

建立了以切削力、主电机功率和声发射为基础的多传感器检测系统，提出了聚合度方法对多传感器特征信息进行筛选，并采用模糊模式识别方法对多传感器信号进行融合。从而实现对加工过程中的刀具状态的实时、可靠识别。     

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.     

On line tool wear monitoring based on auto associative neural network

This paper presents a new tool wear monitoring method based on auto associative neural network. The main advantage of the model lies that it can be built only by the data under normal cutting condition. Therefore, the training samples of the tool wear status are no longer needed during the training process that makes it easier to be applied in real industrial environment than other neural network models. An averaged distance indicator is proposed to denote not only the occurrence of the tool wear but also its severity. Moreover, the Levenberg–Marquardt (LM) training algorithm is introduced to improve the convergence accuracy of the auto associative neural network. Based on the proposed method, a framework for online tool condition monitoring is illustrated and the cutting force data under different tool wear status are collected to simulate the online modeling and monitoring process for the rough and finish milling respectively. The results show that the proposed indicator can reflect the evolution process of tool wear correctly and the LM algorithm is more accurate in comparison with the gradient descent methods. Therefore, it casts new light on practical application of neural network in the field of on line tool condition monitoring.     

Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions

Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions will cause fluctuations in the signal amplitude and thus generate false alarms. This study introduced a novel method for tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions. The amplitude ratio (AR) and the energy ratio (ER) were proposed according to the power spectrum of the spindle vibration signal, which represents the change of amplitude and the energy distribution, respectively. The AR and ER are normalized and integrated into a unified indicator for real-time breakage monitoring. The floating monitoring threshold is designed based on the Gaussian distribution. Moreover, the material removal rate (MRR) is selected as a secondary indicator to accurately identify tool breakage based on determining the amplitude fluctuation caused by cutting conditions or teeth breakage. The effectiveness of the proposed method for tool breakage monitoring has been verified under the constant, time-varying, and entry/exit cutting conditions. The results show that the proposed indicators have higher sensitivity than the traditional root mean square (RMS) features and eliminate false alarms during condition change transients. This research provides a potential solution for tool breakage monitoring under complex cutting conditions.     

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.     

Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.