共查询到20条相似文献,搜索用时 31 毫秒
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P.-C. Tseng W.-C. Teng 《The International Journal of Advanced Manufacturing Technology》2004,24(5-6):404-414
Tool condition monitoring systems play an important role in a FMS system. By changing the worn tool before or just at the time it fails, the loss caused by defect product can be reduced greatly and thus product quality and reliability is improved. To achieve this, an on-line tool condition monitoring system using a single-chip microcomputer for detecting tool breakage during cutting process is discussed in this paper. Conventionally, PC-based monitoring systems are used in most research works. The major shortcoming of PC-based monitoring systems is the incurred cost. To reduce costs, the tool condition monitoring system was built with an Intel 8051 single-chip microprocessor and the design is described in this paper. The 8051 tool monitoring system uses a strain gauge for measuring cutting force; according to the force feature, the tool monitoring system can easily recognize the breakage of the cutting tool with its tool breakage algorithm. The experimental results show that the low-cost 8051 tool monitoring board can detect tool breakage in three successive products successfully. 相似文献
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P. Y. Sevilla-Camacho G. Herrera-Ruiz J. B. Robles-Ocampo J. C. Jáuregui-Correa 《The International Journal of Advanced Manufacturing Technology》2011,53(9-12):1141-1148
Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the I rms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement. 相似文献
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综述了立铣加工刀具状态监控技术的研究现状.刀具状态监控技术在工业上的应用涉及到两种关键系统:一是具有不同智能水平的各种传感器系统,另一个是能把机床控制器所用的控制信号综合成能发觉刀具破损和反映刀具磨损程度信号的过程监控系统.这两种系统相结合,就能实现刀具状态监控系统在工业加工环境的应用. 相似文献
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Dr Xiaoli Li 《The International Journal of Advanced Manufacturing Technology》1998,14(8):539-543
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. 相似文献
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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. 相似文献
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Self-Learning Algorithm for Automated Design of Condition Monitoring Systems for Milling Operations 总被引:1,自引:1,他引:0
A. Al–Habaibeh N. Gindy 《The International Journal of Advanced Manufacturing Technology》2001,18(6):448-459
This paper investigates an approach, termed self-learning ASPS (automated sensor and signal processing selection), aimed at
aiding the systematic design of condition monitoring systems for machining operations. The paper outlines a self-learning
methodology for the classification of the system’s normal and faulty states and the selection of the most appropriate sensors
and signal processing methods for detecting machining faults in end milling. The aim of the proposed approach is to enable
the condition monitoring designer to use previous system faults or incidents to design an on-line monitoring system, reducing
the system’s development time and cost. Force, acceleration and acoustic emission signals are used to design the condition
monitoring systems for end milling operations. Gradual tool wear, catastrophic cutter breakage and tool collision are used
for evaluating the proposed self-learning ASPS approach. The initial results show that the suggested algorithm can be applied
for an automated, self-learning monitoring system for the selection of the most sensitive sensors and signal processing methods
for machining faults and conditions. 相似文献
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Xiaozhi Chen Beizhi Li 《The International Journal of Advanced Manufacturing Technology》2007,33(9-10):968-976
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. 相似文献
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Kang-Ning Lou Dr Cheng-Jen Lin 《The International Journal of Advanced Manufacturing Technology》1997,13(8):556-565
The objective of this paper is to construct an intelligent sensor fusion monitoring system for tool breakage on a machining centre. Since none of the sensing and diagnosis techniques have proved to be completely reliable in practice, an intelligent tool-monitoring system consisting of a neural-network-based algorithm and a sensor fusion system is proposed. The dual sensing signals of cutting force and acoustic emission are used simultaneously in the proposed system owing to good correlation existing between them, and, a self-learning neural-network algorithm is used to integrate multiple sensing information to make a proper decision about tool condition. The results show good performance in tool-breakage detection by the proposed monitoring system, especially where there is high interference. 相似文献
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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。 相似文献
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Yao-Wen Hsueh Chan-Yun Yang 《The International Journal of Advanced Manufacturing Technology》2008,37(9-10):872-880
A new approach is proposed using a support vector machine (SVM) to classify the feature of the cutting force signal for the prediction of tool breakage in face milling. The cutting force signal is compressed by averaging the cutting force signals per tooth to extract the feature of the cutting force signal due to tool breakage. With the SVM learning process, the output of SVM’s decision function can be utilized to identify a milling cutter with or without tool breakage. Experimental results are presented to verify the feasibility of this tool breakage prediction system in milling operations. 相似文献
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Balla Srinivasa Prasad M. M. M. Sarcar B. Satish Ben 《The International Journal of Advanced Manufacturing Technology》2010,51(1-4):57-67
In automated manufacturing systems, one of the most important issues is accurate detection of the tool conditions under given cutting conditions so that worn tools can be identified and replaced in time. In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. But the effects of vibrations have been paid less attention. In the present paper, an investigation is presented of a tool condition monitoring system, which consists of a fast Fourier transform preprocessor for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. A fast Fourier transform (FFT) can decompose AOE signals into different frequency bands in the time domain. Present work uses a laser Doppler vibrometer for online data acquisition and a high-speed FFT analyser used to process the AOE signals. The generation of the AOE signals directly in the cutting zone makes them very sensitive to changes in the cutting process due to vibrations. AOE techniques is a relatively recent entry into the field of tool condition monitoring. This method has also been widely used in the field of metal cutting to detect process changes like displacement due to vibration and tool wear, etc. In this research work the results obtained from the analysis of acousto-optic emission sensor employs to predict flank wear in turning of AISI 1040 steel of 150 BHN hardness using Carbide insert and HSS tools. The correlation between the tool wear and AOE parameters is analyzed using the experimental study conducted in 16 H.P. all geared lathe. The encouraging results of the work pave the way for the development of a real-time, low-cost, and reliable tool condition monitoring system. A high degree of correlation is established between the results of the AOE signal and experimental results in identification of tool wear state. 相似文献
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D Choi W. T Kwon C. N Chu 《The International Journal of Advanced Manufacturing Technology》1999,15(5):305-310
The sensor fusion method using both an acoustic emission (AE) sensor and a built-in force sensor is introduced for on-line
tool condition monitoring during turning. The cutting force was measured by a built-in piezoelectric force sensor, which was
inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for
the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A significant drop in cutting
force indicated tool breakage. The algorithm was implemented in a DSP board and the monitoring system was installed on a CNC
lathe in an FMS line for in-process tool-breakage detection. The proposed system showed an excellent monitoring capability. 相似文献
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Pedro Daniel Alaniz-Lumbreras Roberto Augusto Gómez-Loenzo René de Jesús Romero-Troncoso Rebeca del Rocío Peniche-Vera Juan Carlos Jáuregui-Correa 《Machining Science and Technology》2013,17(2):263-274
One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed. 相似文献
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Pedro Daniel Alaniz-Lumbreras Roberto Augusto G mez-Loenzo Ren de Jesú s Romero-Troncoso Rebeca del Rocí o Peniche-Vera Juan Carlos J uregui-Correa Gilberto Herrera-Ruiz 《Machining Science and Technology》2006,10(2):263-274
One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed. 相似文献