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基于B样条模糊神经网络的刀具磨损监测 总被引:2,自引:0,他引:2
刀具状态监测是实现自动化加工和无人化加工的关键技术。本文使用切削力和声发射传感器监测金属切削过程,提出了基于B样条模糊神经网络作为刀具磨损量监测模型。该模型能够准确描述刀具磨损和信号特征之间的非线性关系,和常用的BP前馈神经网络相比,具有收敛速度快和局部学习能力等优点。试验结果表明:采用B样条模糊神经网络对提高刀具磨损在线监测的准确度和可靠度非常有效。 相似文献
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为提高铣削加工时的刀具利用率、降低刀具成本,提出采用机器视觉技术在机监测铣刀磨损状态,及时更换刀具。建立刀具磨损监测系统,由电荷耦合器件(Charge coupled device,CCD)相机获取刀具磨损图像,通过图像预处理、阈值分割、基于Canny算子和亚像素的边缘检测方法建立刀具磨损边界,提取刀具磨损量。开展GH4169镍基高温合金铣削实验,将监测系统检测的磨损量与超景深显微镜的测量结果进行比对,结果表明:该系统具有较高的检测精度,可实现铣削加工时刀具磨损状态的在机监测。 相似文献
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《计算机集成制造系统》2014,(1)
针对由离散刀位构成的一阶线性不连续刀路影响机床运动平稳性的问题,提出在工件坐标系下对线性刀路进行光顺,获取满足精度且达到G1以上连续的等距双非均匀有理B样条刀具路径。采用对偶四元数描述五轴刀位,利用对偶四元数B样条矢量函数拟合线性刀路,获取五轴联动的刀具B样条运动轨迹,插值得到描述刀具中心点和刀轴点运动轨迹的等距双非均匀有理B样条曲线刀具路径。算例表明,采用本算法对工件坐标系中的线性刀路进行光顺,在提高刀具轨迹运动平稳性的同时,可以获得满意的加工精度,适用于五轴高速精密加工。 相似文献
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基于加工表面盒维数的刀具磨损状态研究 总被引:1,自引:0,他引:1
在车削加工过程中,随着刀具磨损量的增加,在工件表面的纹理结构发生变化,依据工件纹理的变化能够间接判断刀具的磨损程度。将分形理论引入到基于图像的刀具状态监测领域,研究二维离散图像信号盒维数的具体实现算法以及盒维数与刀具磨损量之间的变化关系。实验表明:随着刀具磨损量的增加,盒维数具有缓慢上升的趋势,利用这一特征可有效实现刀具磨损状态的监测。 相似文献
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《计算机集成制造系统》2015,(6)
针对由离散刀位点数据生成的五轴加工等距非均匀有理B样条曲线刀具路径中两条非均匀有理B样条曲线参数不同步的问题,建立了一种等距双非均匀有理B样条曲线参数的同步模型,实现等距双非均匀有理B样条曲线刀具路径插补过程中两条非均匀有理B样条曲线参数的同步运动。将三轴非均匀有理B样条曲线插补算法应用于五轴加工等距双非均匀有理B样条曲线刀具路径中的刀具中心点非均匀有理B样条曲线,根据参数同步模型将插补算法同步到刀轴点非均匀有理B样条曲线中。仿真表明,采用该算法能够使刀具始终沿着等距双非均匀有理B样条曲线刀路进行同步插补,参数同步插补精度主要集中在10-6 mm级数上,适用于复杂曲面的五轴加工。 相似文献
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刀具磨损监测是推动数控机床稳定运行的有效手段。为实现刀具健康实时监测,设计一种基于长短记忆网络的刀具磨损监测方法。将实时采集的切削力信号进行去噪、降维等预处理;用堆叠自编码器进行特征提取,获得影响刀具磨损的本质特征;用长短记忆网络构建刀具磨损监测模型,实现加工过程中刀具磨损监测。利用铣削实验数据进行实例验证,获得磨损量平均绝对误差为0.060 80的铣刀磨损量监测模型,验证了该方法的有效性。 相似文献
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针对刀具的早期故障监测中因存在强烈的背景噪声而难以提取故障特征的问题,提出了基于二次采样随机共振消噪和B样条神经网络智能识别的故障诊断方法。首先利用在随机共振过程中,噪声增强振动信号的信噪比特性,将刀具振动信号进行随机共振输出,提取有效特征,再输入到B样条神经网络进行智能识别,进而获得刀具的磨损值。同时,为了得到与输入信号最佳匹配的随机共振参数,提出了基于遗传算法的多参数同步优化的自适应随机共振算法,克服了传统随机共振系统只实现单参数优化的缺点。实验结果表明,该方法能实现弱信号检测,能有效地应用于刀具磨损故障诊断中。 相似文献
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An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis
《Measurement》2016
We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates. 相似文献
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X. M. Lai A. H. Luo Y. S. Zhang G. L. Chen 《The International Journal of Advanced Manufacturing Technology》2009,40(3-4):226-233
In the last decade, the progress of surface metrology has led to improved 3D characterisation of surfaces, offering the possibility of monitoring manufacturing operations and providing highly detailed information regarding the machine tool condition. This paper presents a case study where areal surface characterisation is used to monitor tool wear in peripheral milling. Due to the fact that tool wear has a direct effect on the machined workpiece surface, the machined surface topography contains much information concerning the machining conditions, including the tool wear state. By analysing the often subtle changes in the surface topography, one can highlight the tool wear state. This paper utilises areal surface characterization, areal auto-correlation function (AACF) and pattern analysis to illustrate the effect of tool wear on the workpiece surface. The result shows the following: (1) tool wear, previously difficult to detect, will influence almost all of the areal surface parameters; (2) the pattern features of AACF spectrum can reflect the subtle surface texture variation with increasing tool wear. The authors consider that, combined analysis of the surface roughness and its AACF spectrum are a good choice for monitoring the tool wear state especially with the latest developments in on-machine surface metrology. 相似文献
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Tae Jo Ko Dong Woo Cho 《The International Journal of Advanced Manufacturing Technology》1996,12(1):5-13
An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring 相似文献
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数控机床刀具磨损监测方法研究 总被引:2,自引:0,他引:2
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.文章有针对性地回顾了国内外各种刀具磨损监测方法的研究工作,详细叙述了切削力监测法、切削噪声监测法、功率监测法、声发射监测法、电流监测法以及基于多传感器监测法等六种刀具磨损监测方法.本文通过比较各种监测方法的优缺点,提出基于多传感器监测法是数控机床刀具磨损监测方法的未来发展的主要方向. 相似文献