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1.
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. 相似文献
2.
Y. M. Niu Dr Y. S. Wong G. S. Hong 《The International Journal of Advanced Manufacturing Technology》1998,14(2):77-84
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
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2
variance
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k
n
end condition factor of the cantilever beam
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E
Young's modulus of tool holder
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I
moment of inertia of tool holder at cross section
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m
mass of tool holder per unit length
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L
length of tool overhang
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l
the size of the moving window
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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)
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fs, ps, ss, ks
the standard deviation values of the four primary features
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F=
resultant feature vector
ART2 neural network parameters
I
i
element of input vector
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Y
i
output node
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W
i
,X
i
,U
i
,V
i
,P
i
,Q
i
parameters inF
1 layer
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R
i
orienting parameter
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vigilance parameter
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b
ij
,t
ji
bottom-to-top and top-to-bottom weights
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a, b, c
network parameters
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f()
thresholding function 相似文献
3.
数控机床刀具磨损监测实验数据处理方法研究 总被引:3,自引:0,他引:3
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. 相似文献
4.
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。 相似文献
5.
Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization. 相似文献
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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. 相似文献
8.
Rodrigo Henriques Lopes da Silva Márcio Bacci da Silva Amauri Hassui 《Machining Science and Technology》2016,20(3):386-405
Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system. 相似文献
9.
Y. Choi R. Narayanaswami A. Chandra 《The International Journal of Advanced Manufacturing Technology》2004,23(5-6):419-428
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. 相似文献
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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。 相似文献
12.
M. Rahman Q. Zhou Dr G. S. Hong 《The International Journal of Advanced Manufacturing Technology》1995,10(2):87-92
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. 相似文献
13.
M.K. Tsai B.Y. Lee S.F. Yu 《The International Journal of Advanced Manufacturing Technology》2005,26(7-8):711-717
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%. 相似文献
14.
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。 相似文献
15.
Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number. 相似文献
16.
基于卷积神经网络的刀具磨损在线监测 总被引:1,自引:0,他引:1
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。 相似文献
17.
神经网络自适应控制的研究进展及展望 总被引:5,自引:0,他引:5
张秀玲 《工业仪表与自动化装置》2002,(1):10-14
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。 相似文献
18.
Adam G. Rehorn Jin Jiang Peter E. Orban E.V. Bordatchev 《The International Journal of Advanced Manufacturing Technology》2005,26(7-8):942-710
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 相似文献
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