Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

An intelligent sensor system approach for reliable tool flank wear recognition

An intelligent sensor system approach for reliable flank wear monitoring in turning is described. Based on acoustic emission and force sensing, an intelligent sensor system integrates multiple sensing, advanced feature extraction and information fusion methodology. Spectral, statistical and dynamic analysis have been used to determine primary features from the sensor signals. A secondary feature refinement is further applied to the primary features in order to obtain a more correlated feature vector for the tool flank wear process. An unsupervised ART2 neural network is used for the fusion of AE and force information and decision-making of the tool flank wear state. The experimental results confirm that the developed intelligent sensor system can be reliably used to recognise the tool flank wear state over a range of cutting conditions.Notation mean - ² variance - k _n end condition factor of the cantilever beam - E Young's modulus of tool holder - I moment of inertia of tool holder at cross section - m mass of tool holder per unit length - L length of tool overhang - l the size of the moving window - fm, pm, sm, km the mean values of the four primary features (the tangential force component, the frequency band power, the skew, and the kurtosis) - fs, ps, ss, ks the standard deviation values of the four primary features - F= resultant feature vector ART2 neural network parameters I _i element of input vector - Y _i output node - W _i ,X _i ,U _i ,V _i ,P _i ,Q _i parameters inF ₁ layer - R _i orienting parameter - vigilance parameter - b _ij ,t _ji bottom-to-top and top-to-bottom weights - a, b, c network parameters - f() thresholding function     

数控机床刀具磨损监测实验数据处理方法研究

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.有针对性地回顾了国内外各种分析刀具磨损信号方法的研究工作,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的实现形式.通过比较各种数据处理方法的优缺点,提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来发展的主要方向.     

基于径向基函数神经网络的铣刀磨损监控技术的研究

提出一种基于径向基函数神经网络的铣刀磨损监控方法,径向基函数神经网络的输出是刀具磨损的具体值,这样有利于对刀具磨损进行各种实时补偿。实验表明,利用径向基函数神经网络进行状态识别可对小型立铣刀的磨损进行监控,能够取得良好的效果,同时证明RBF网络的训练速度优于BP网络。     

异形螺杆数控铣削刀具磨损智能建模研究

介绍了一种在线估算螺杆数控铣削中刀具磨损量的新方法。该方法基于螺杆铣削过程变切削参数的工况,提取了振动信号和功率信号的刀具磨损特征值,基于自适应神经—模糊推理系统建立了刀具磨损数学模型。实验证明,由此建立的刀具磨损模型能够排除切削参数变化的干扰,可以较好的反映加工中刀具磨损状态,同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

微铣削刀具磨损研究现状

在微制造领域,微铣削因具有加工材料的多样性和能实现三维曲面加工的独特优势而受到越来越多学者的关注,但是微铣刀的快速磨损严重影响了微铣削技术的应用.研究表明微铣刀的磨损主要发生在刀尖部位,刀具磨损呈现显著的尺度效应.分析了微铣刀的磨损机理、刀具磨损的影响因素和改善措施以及刀具磨损状态的监控,并指出了今后研究值得注意的发展方向.     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

基于复映测量的球头刀具磨损数据处理及其软件开发

在铣削加工中,刀具磨损对产品加工质量有重要的影响,以球头刀具磨损为研究对象,为了建立刀具磨损模型,采用复映测量手段获取刀具磨损,在复映测量获得刀具磨损的过程,针对复印测量的球头铣削刀具磨损数据提出了数据处理的方法,并以Visual C++为开发平台实现了基于复映测量的刀具磨损数据处理模块,为基于复映测量的刀具磨损建模提供了基础,所提出的刀具磨损数据处理方法可有效对刀具磨损数据进行分析建立刀具磨损模型。     

应用PC和80C196KC单片机的刀具磨损状态监测系统

设计一种融合声发射(AE)、主轴电动机电流和Z向进给电动机电流多特征参数检测方法的、以PC机为上位机、以80C196KC单片机为下位机的刀具磨损监测系统。主要介绍系统的硬件结构,阐述系统中多路信号采集装置硬、软件工作原理与设计中的关键技术,以及具有辨识功能的上位数据处理计算机的软件工作流程。     

On-line cutting state recognition in turning Using a neural network

Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate.     

A predicted modelling of tool life of high-speed milling for SKD61 tool steel

This paper presents an abductive network for predicting tool life in high- speed milling (HSM) operations. The abductive network is composed of a number of functional nodes. These functional nodes are well organised to form an optimal network architecture by using a predicted squared error criterion. Once the cutting speed, feed per tooth, and axial depth of cut are given, tool life can be predicted based on the developed network. Experimental results have shown that the abductive network can be used to predict HSM end mill life under varying cutting conditions and the prediction error of HSM tool life is less than 10%.     

间歇过程的神经网络模型

间歇过程通常具有非线性，时变和易燃易爆的特点，用常规的建模方法建立起模型比较困难，本文针对间歇聚丙烯过程，利用前馈神经网络建立其数学模型。首先根据实际系统的输入输出建立网络的结构。再用经验数据对网络进行训练，并用未参加训练的数据对网络进行测试，测试的最大误差是0.03MPa，这一误差在要求的范围之内。     

基于卷积神经网络的刀具磨损在线监测

为了提高刀具磨损在线监测的精度和泛化性能,提出一种基于卷积神经网络的刀具磨损量在线监测模型。利用时域传感器信号对刀具磨损量进行定量分析,避免数据预处理带来的信息丢失;采用深度网络自适应地提取特征,取代传统的人工特征提取过程,并通过加深网络进一步挖掘信号中隐藏的微小特征。实验结果表明,该模型对刀具后刀面磨损量监测效果较好,可以有效避免人为特征提取的局限,精度和泛化性都有一定程度的提高。与相关研究的对比也证实了其可行性和有效性。     

神经网络自适应控制的研究进展及展望

关于人工神经网络与自适应结合的研究,近年来已成为智能控制学科的热点之一。自适应具有强鲁棒性,神经网络则具有自学习功能和良好的容错能力,神经网络自适应控制由于较好地结合了二者的优点而具有强大的优势。本文系统地综述了神经网络自适应控制的进展,讨论了神经网络自适应的主要模型和算法,并就其存在的一些问题、应用与发展趋势进行了探讨。     

State-of-the-art methods and results in tool condition monitoring: a review

This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research. An erratum to this article can be found at     

基于小波包和BP神经网络的刀具磨损状态识别

刀具磨损状态影响金属切削过程,因此监测刀具磨损状态对提高产品质量有着重要的意义。设计刀具磨损状态监测系统,利用传感器采集刀具振动信号,通过小波包对振动信号进行数据分析,并把不同频段的能量值作为刀具磨损状态的特征值,建立BP神经网络,从而在刀具磨损状态和振动信号特征向量之间建立映射关系,完成刀具磨损状态的监测。利用C++Builder和Matlab软件混合编程实现了系统的功能。试验表明,系统运行良好,能够对刀具磨损状态进行正确识别。     

A neural network application for reliability modelling and condition-based predictive maintenance

Traditionally, decisions on the use of machinery are based on previous experience, historical data and common sense. However, carrying out an effective predictive maintenance plan, information about current machine conditions must be made known to the decision-maker. In this paper, a new method of obtaining maintenance information has been proposed. By integrating traditional reliability modelling techniques with a real-time, online performance estimation model, machine reliability information such as hazard rate and mean time between failures can be calculated. Essentially, this paper presents an innovative method to synthesise low level information (such as vibration signals) with high level information (like reliability statistics) to form a rigorous theoretical base for better machine maintenance.     

Assessment and visualisation of machine tool wear using computer vision

Tool wear monitoring is an integral part of modern CNC machine control. Cutting tools must be periodically checked for possible or actual premature failures, and it is necessary to record the cutting history for a tool’s full life of utilisation. This means that an on-line monitoring system would be of great benefit to overall process control in manufacturing systems. Computer vision has already shown promise as a candidate technology for this task. In this paper, we describe the use of digital image processing techniques in the analysis of images of worn cutting tools in order to assess their degree of wear and thus remaining useful life. It is shown that a processing strategy using a variety of image texture measures allows for effective visualisation and assessment of tool wear, and indicates good correlation with the expected wear characteristics.     

Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation

A tool wear monitoring system is indispensable for better machining productivity, with the guarantee of machining safety by informing of the time due for changing a tool in automated and unmanned CNC machining. Different from monitoring methods using other signals, the monitoring of the spindle current has been used without requiring additional sensors on the machine tools. For reliable tool wear monitoring, only the current signal from tool wear should be extracted from the other parameters to avoid exhaustive analyses on signals in which all of the parameters are fused together. In this paper, the influences of force components from different parameters on the measured spindle current are investigated, and a hybrid approach to cutting force regulation is employed for tool wear signal extraction from the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects, varied to regulate the force component from tool wear.