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螺纹刀具的状态监测是制造加工中非常重要的问题.由于刀具振动信号具有复杂非线性、强耦合等关系,常用的基于支持向量机(SVM)的刀具监测模型由于参数的设置极其依赖人为经验,设置不当会导致监测的识别率不高,在刀具磨损状态判别中收到了限制.针对此难题,依据螺纹刀具的振动特性,结合改进的粒子群算法(PSO),采用异步更新学习因子策略实现刀具状态监测模型优化.结果表明,优化后的PSO-SVM刀具状态监测模型能够有效对SVM的关键参数进行寻优,异步更新学习因子也可加强模型在迭代后期的寻优能力,从而提高刀具状态监测识别的精度. 相似文献
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刀具在加工过程中不可避免的存在着磨损和破损现象,刀具的消耗直接导致工件精度下降和生产成本增加。开展了一系列实验,深入研究刀具状态监测方法,构建了新型铣削过程刀具磨损监测试验系统。通过振动传感器和声发射传感器对铣削过程中不同磨损程度刀具的信号进行检测、采集、分析。选择对刀具磨损状态反映敏感的特征量。采用BP神经网络,建立刀具磨损特征向量与刀具磨损状态之间的非线性映射关系。 相似文献
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刀具切削状态的在线监测是自动化加工技术中的难题,国内外已有许多专家和学者进行了深入研究。针对目前刀具切削状态在线监测中出现的问题,提出了一种利用小波分析技术提取刀具状态信号特征量,对刀具的破损进行预报的监测系统。经大量试验和使用,验证了其可行性和实用性。 相似文献
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刀具切削状态的电机电流监测新方法 总被引:4,自引:0,他引:4
本文从实时在线监测刀具切削状态的应用出发,研制了一种新型的电机电流拾取方法和传感装置,并分析了刀具在加工过程中切削力,电机电流与刀具切削状态的关系,同时,研究了利用电机电流信号进行刀具切削状态的监测原理,实验证明了其方法的可靠性和实用性。 相似文献
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在实际切削加工中刀具磨损的全状态先验知识获取困难,而刀具磨钝状态下的先验知识则较易获取。针对这种不完备先验知识情况,以切削力为监测信号,提出基于连续隐马尔可夫模型(CHMM)的刀具磨损状态评估技术。应用小波包分解技术提取信号特征信息,利用刀具磨钝状态下的先验归一化特征信息建立CHMM监测模型;根据刀具未知状态特性向量与监测模型间的对数似然度获取刀具性能指标,实现刀具磨损状态评价。铣刀全寿命磨损实验表明:该方法能在仅具备磨钝状态先验知识情况下,实现对刀具的磨损状态的初步评估,且所需样本数较少,训练速度快。 相似文献
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通过人工智能、工业大数据实时感知切削加工中的刀具状态是实现面向性能的制造的重要技术途径,也是高性能制造的关键内涵。然而,在目前的切削刀具状态监测算法中,特征提取过程多依赖于人工经验,这无疑限制了刀具状态监测技术的在高性能制造中的推广应用。因此,针对高性能加工监测中的自主性和准确性要求,基于特征自适应融合和集成学习技术,提了出一种面向高性能铣削的刀具磨损监测方法。所提出的监测方法能够根据特征的表现自动为其赋予不同的权重从而实现特征的自适应融合,同时利用AdaBoost集成学习算法,在自动融合特征的同时保证了状态监测精度。薄壁件铣削实验表明,监测结果与真实磨损间的RMSE和MAE值最大为10.44,最小可达5.16。所提出的方法能够自主、准确地监测航空类薄壁件铣削加工中的刀具磨损状态,解决了高性能铣削加工刀具磨损监测中的人工经验依赖问题。 相似文献
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Paul W. Prickett Raees A. Siddiqui Roger I. Grosvenor 《The International Journal of Advanced Manufacturing Technology》2011,55(9-12):855-867
The monitoring of end milling cutting operations for tool breakage is achieved using a low-cost microcontroller-based system. The system is based upon acquiring and analysing machine tool-based signals for characteristic responses to tool breakage. Spindle speed and load signals are shown to be responsive to tool condition and thus capable of supporting the deployed approach. The resulting system operates in real time with tool breakage detection consistently diagnosed within two revolutions. The monitoring function is extended to consider tool wear using analysis methods applied in the time and frequency domains. Decisions about tool condition are made by integrating all relevant information into a rule base. Higher-level tool management functions supported by the deployed system are identified. 相似文献
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Miron Zapciu Jean-Yves K��nevez Alain G��rard Olivier Cahuc Claudiu Florinel Bisu 《The International Journal of Advanced Manufacturing Technology》2011,57(1-4):73-83
In the working space model of machining, an experimental procedure is implemented to determine the elastic behaviour of the machining system. In this paper, a dynamic characterization and vibration analysis has long been used for the detection and identification of the machine tool condition. The natural frequencies of the lathe machining system are required (Ernault HN400??France) according to three different situations with no cutting process are acquired. The system modal analysis is used to identify the natural frequencies. These frequencies are then compared to the ones obtained on the spindle numerical model by finite element method. This work is validated by experimental tests based on measures of the lathe machine tool frequencies domain. The main objective is to identify a procedure giving the natural frequency values for the machine tool components, in order to establish a better condition in the cutting process of the machine tool. 相似文献
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K. F. Martin P. Thorpe 《The International Journal of Advanced Manufacturing Technology》1990,5(1):66-85
A new methodology is described for the condition monitoring and fault diagnosis of machine tool coolant systems. The steady
state characteristics of the coolant system pump outlet pressure, pump motor temperature and tank level are used to define
health parameters from which the system health is deduced. On detection of a significant change in system health the pressure
transient on closure of the flow valve is captured to aid diagnosis of the fault.
A demonstration system for the coolant system of a Wadkin V4-6 machine tool is described. This is used to verify the usefulness
of the health system described above by simulation of a number of typical faults. 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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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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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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The Application of an ANFIS and Grey System Method in Turning Tool-Failure Detection 总被引:6,自引:0,他引:6
S.-P. Lo 《The International Journal of Advanced Manufacturing Technology》2002,19(8):564-572
The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure.
This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure
detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored
by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force
signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined
signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we
adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding
the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect
to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell
MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five
cutting parameters on tool-state detection. 相似文献
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利用小波变换模的极大值和信号奇异点的关系,分析了用Lip指数来描述的切削力信号局部奇异性.通过观察奇异点的位置等信息得到切削刀具的磨损情况.通过对实际刀具磨损的在线监测数据分析,证明了采用小波变换检测刀具磨损这一方法的有效性. 相似文献
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R. Benhadj S. Sadeque H. Rahnejat 《The International Journal of Advanced Manufacturing Technology》1988,3(1):77-102
This paper outlines the integration of a two-dimensional vision system with a pneumatic proximity-to-tactile sensing device
to form a Co-ordinated ‘Hand-Eye’ system. With the aid of a knowledge base this system is utilised as an intelligent condition
monitoring tool for recognition and detection of orientation of parts in a flexible manufacturing environment. An expert system
is formulated to interrogate the acquired data streams for the purpose of comparative studies with the knowledge base. Appropriate
data processing methods are employed to ensure rapid manipulation of data for real-time applications. 相似文献