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表面粗糙度模糊神经网络在线辨识模型 总被引:8,自引:0,他引:8
为解决零件加工中表面粗糙度在线检测困难这一问题,提出一种基于模糊神经网络的零件表面粗糙度在线辨识方法,并以外圆纵向磨削为例,建立表面粗糙度模糊神经网络在线辨识模型.首先研究前人建立的外圆纵向磨削零件表面粗糙度理论公式及经验公式,得出加工中的工件速度、砂轮速度、磨削深度和纵向进给量对零件表面粗糙度有直接影响,并进一步提出以在线测得的加工中工件与砂轮的速度比、磨削深度和纵向进给量作为零件表面粗糙度辨识模型的输入.由于加工过程极其复杂,无法建立加工中零件表面粗糙度与加工参数之间的精确数学模型,故将模糊神经网络引入建模过程中.同时,由于加工中零件表面粗糙度的对数与加工参数的对数存在线性关系,故模型中采用了T-S型模糊推理.此模型应用于实际磨削加工中,建模型精度可达97%,这进一步证明此在线辨识方法的可行性. 相似文献
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基于激光三角法的零件表面粗糙度在线测量 总被引:1,自引:0,他引:1
介绍了一种零件表面粗糙度的激光在线测量方法,该测量方法具有测量速度快且能够显示被测表面的具体形貌等优点.在测量中引入激光三角测量系统,用无衍射激光光束作光源,用高精度的摄像机作位移传感器,通过计算机数据处理得到表面粗糙度值,使表面粗糙度在线检测成为可能。 相似文献
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在理论分析和实验研究的基础上,提出了一种在线检测磨削表面粗糙度的新方法,该方法利用声发射(AE)传感器探头与磨削表面摩擦产生的AE信号在线检测磨削表面粗糙度。并通过实际跟踪到测试证明了该方法的可行性。 相似文献
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车削参数直接影响零件的表面粗糙度.文中提出基于神经网络理论的车削表面粗糙度实时预测技术,并给出了实现的步骤和部分参考数据.为实现数控系统的切削参数自动在线优化提供了技术基础. 相似文献
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杨桂林 《现代制造技术与装备》2008,(2):23-25
基于模糊神经网络建立了磨料水射流精密切割的模型.工件厚度、磨料流量、加工表面的粗糙度和射流压力三个是模糊神经网络的输入,切割速度是模糊神经网络的输出.以JJ-Ⅰ水射流切割机床为实验装置,YL12硬铝作为试件材料进行实验研究,获取样本数据.试件表面粗糙度用TR200粗糙度仪测试.在MATLAB上用样本数据对模型进行训练.用训练好的模型计算给定条件下磨料水射流的切割速度,并以该速度进行线性切割.获得的切割表面粗糙度值基本符合给定的精度要求. 相似文献
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在线微磨损检测技术的应用 总被引:1,自引:0,他引:1
本文叙述了在线表面粗糙度检测技术在动态微磨损研究中的应用。通过设计环块试验机模拟部分膜弹性流体动力润滑(PEHL)下的微磨损工况,考察了在线检测技术在润滑条件下对微磨损检测的有效性。该套技术成功地在不停机的情况下测得微磨损过程中表面粗糙度的变化,并通过计算获得了磨损表面的微磨损量。该项技术不仅可以应用到对PEHL下动态磨损的研究,也可应用到一些运动工件表面形貌的检测。 相似文献
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S.-J. Lou J. C. Chen 《The International Journal of Advanced Manufacturing Technology》1999,15(3):200-209
This paper describes a new approach for surface roughness recognition (ISRR) systems to predict surface roughness (Ra) in-process
using an accelerometer to measure vibration signals and cutting conditions while end-milling is taking place. The analysis
of the data and the model building is carried out using a neural fuzzy system. Experimental results show that the parameters
of spindle speed, feedrate, depth of cut, and vibration variables can predict the surface roughness (Ra) effectively. Surface
roughness can also be predicted with a 96% accuracy rate by ISRR using the neural fuzzy system. 相似文献
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Samson S. Lee Joseph C. Chen 《The International Journal of Advanced Manufacturing Technology》2003,22(7-8):498-509
In modern manufacturing environments, the quality assurance of machined parts has attracted great attention from manufacturers. The surface roughness of a workpiece is one of the most important factors to consider. The need for developing a surface recognition system that is able to replace stylus-style surface measuring systems has increased to improve the efficiency of production. In this research an on-line surface recognition system was developed based on artificial neural networks (OSRR-ANN) using a sensing technique to monitor the effect of vibration produced by the motions of the cutting tool and workpiece during the cutting process. Different combinations of cutting conditions were conducted to develop an OSRR system for a lathe. In order to determine the direction of the vibration which most significantly affects surface roughness, a triaxial accelerometer was employed. Three directional vibrations which were detected simultaneously by the accelerometer were analyzed using a statistical method. The radial direction vibration was found to be the most significant vibration in turning operations. The accuracy of the developed systems showed that the developed system could predict surface roughness efficiently. The developed system not only proposes a surface recognition system which is alternative to that using a traditional measurement instrument, but also provides an on-line surface recognition system for turning operations. 相似文献
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从放在工件夹具上的声发射(AE)传感器测得的磨削加工中的AE信号中,提取有关磨削表面粗糙度的信息,用神经网络的方法对高速深切平面磨削工程陶瓷氮化硅工件表面粗糙度进行了在线连续监测.结果表明,该方法基本可行,通过进一步改进,可以用于磨削工程陶瓷工件表面粗糙度的在线监测,为磨削智能化打下基础. 相似文献
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基于图像清晰度评价的磨削表面粗糙度检测方法 总被引:3,自引:2,他引:1
针对当前机器视觉检测粗糙度主要采用图像灰度值信息进行统计分析,没有充分利用色彩信息且忽略了人眼视觉系统主观评判的问题,提出一种基于图像清晰度评价的磨削表面粗糙度检测方法。根据色块在不同等级粗糙度表面上形成的图像清晰度不一样,采用熵函数评价算法和基于色彩相关性的彩色图像清晰度评价算法分别构建清晰度与粗糙度之间的关系模型,论证了基于图像清晰度检测磨削表面粗糙度方法的可行性。试验结果表明,基于图像清晰度检测磨削表面粗糙度是一种可行的粗糙度检测方法,清晰度与粗糙度相关性强,清晰度有随着粗糙度的增大而减小的趋势,且利用色彩相关性的彩色图像清晰度评价算法对磨削表面粗糙度检测具有较好的灵敏性,并且该方法符合人眼视觉系统的主观评价;清晰度算法和主观评价二者结合可快速简易地在线检测工件的整体表面轮廓粗糙度。 相似文献
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基于零件加工表面粗糙度在线检测困难问题,应用BP网络建模对外圆磨削加工表面粗糙度值进行预测,并通过实验验证所建模型的正确性,同时也验证了实验数据的准确性。 相似文献
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本文采用模糊PI控制代替传统的PI控制,通过检测电机转速的实际值与给定值的偏差和偏差的变化率,在线查表选择适当的PI参数,实现了永磁同步电机的矢量控制。基于matlab/simulink的仿真结果表明,该方法具有良好的静动态特性。 相似文献
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A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations 总被引:4,自引:1,他引:3
J.C. Chen M. Savage 《The International Journal of Advanced Manufacturing Technology》2001,17(9):670-676
This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness recognition (FN-M-ISRR) system, the
goal of which is to predict surface roughness (Ra ) under multiple cutting conditions determined by tool material, workpiece
material, tool size, etc. Surface roughness was measured indirectly by extrapolation from vibration signal and cutting condition
data, which were collected in real-time by an accelerometer sensor. These data were analysed and a model was constructed using
a neural fuzzy system. Experimental results showed that parameters of spindle speed, feedrate, depth of cut, and vibration
variables could predict surface roughness (Ra) under eight different combinations of tool and workpiece characteristics. This
neural fuzzy system is shown to predict surface roughness (Ra ) with 90% prediction accuracy during a milling operation. 相似文献
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This paper describes an on-line system for digital analysis of surfaces where the electrical signal from a surface roughness instrument is converted and input into a desk-top computer which also controls the traversing. Special fixtures integrated with the system and dedicated software permit two- and three-dimensional tracings to be carried out on plane as well as on cylindrical parts. The surface profiles can be plotted in different ways. Roughness parameters, frequency spectra etc, can be computed digitally and all results can be stored. Scratches and irregularities on the surface can be identified and processed separately from the rest of the surface. The desk-top computer is connected to the university mainframe computer through a modem, this being of advantage in connection with graphic data processing. The capability of the equipment has been investigated and results from calibration are presented. 相似文献