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基于加工过程中刀具产生的动态信号,利用BP神经网络多输入、多输出和非线性映射的特性,通过融合多种加工特征信号,建立了切削参数与加工动态过程之间的关系模型,实现了刀具在线加工状况的检测与预报。仿真结果表明,基于工况信息融合的神经网络刀具监控方法不但可以减少加工参数变化对刀具状态检测的影响,而且提高了在线检测刀具磨损量的精确度,验证了该方法的有效性。 相似文献
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基于电流信号钻头磨损状态预报系统 总被引:1,自引:0,他引:1
介绍了一个钻头磨损状态在线预报系统。通过监测主电机电流信号建立神经网络动态预报模型 ,对钻头后刀面磨损量分类建立在不同磨损类别下的神经网络模型 ,以神经网络模型估算的电流值作为模糊聚类中心 ,根据预报电流值对钻头磨损状态进行模糊分类 ,从而预报磨损状态。实验表明 ,此预报系统具有较高的成功率和可靠性 相似文献
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刀具状态检测可以有效降低加工过程中刀具的不确定性,提高数控加工质量和效率,降低加工成本。在小批量制造模式下的复杂零件制造过程中,零件的几何形状和加工参数不断变化,刀具所受外力也在不断改变,进而导致刀具磨损速率持续变化。传统的固定切削时间更换刀具的方法只能采取更加保守的切削时间更换刀具,给加工过程增加了很多的不确定性,并造成严重的刀具浪费。本文针对以上问题提出了一种刀具磨损在线测量方法,通过电子显微镜在线拍摄刀具照片,经小波滤波降噪处理后的图片由卷积神经网络进行处理,并自动计算出刀具磨损量。该方法可以有效地提取出刀具磨损量,测量误差不超过0.02mm。 相似文献
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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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<正> 车削加工中的刀具磨损问题长期以来一直引起人们的普遍关注。这不仅因为刀具本身的成本将影响总的加工成本,而且因为刀具的更换增加了机床辅助时间,在生产效率极大提高的今天,后一因素对总的加工成本有更大的影响。刀具的磨损率是切削用量提高的一个主要制约因素,而它们又共同决定着总的加工成本(当然还有其它一些影响因素)。因此,如何确定一个最为合理的切削条件就不能不考虑刀具的磨损问题。车刀磨损量与磨损率的在线监测对于车削过程的动态最佳化与数控车床的优化自适应控制是一个必要的前提,它对于最大限度地发挥车床潜力,提高经济效益有重要意义。此外,如果我们能在线预报车刀的磨损离急剧阶段还 相似文献
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为实现高速加工时刀具渐变磨损状态的在线准确识别,提出了一种集合多种智能的间接检测刀具磨损状态方法的模糊数据融合方法。尽管这些方法具有算法实现较为简单、处理速度较快的优点,但单一的信号检测及单一的智能建模方法难以获得全面的加工状态信息和准确的识别结果。为此,利用F推理技术对上述方法的冗余和互补信息进行数据融合,应用Makino—Fanuc 74-A20型加工中心的测试数据验证了该方案的可行性,并将刀具后刀面磨损的预测值与基于机器视觉检测的实测值进行比较。实验结果分析表明,多参数模糊融合识别方法能快速获得切削刀具磨损状态更加准确的预测值。 相似文献
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X. Li S. Dong P.K. Venuvinod 《The International Journal of Advanced Manufacturing Technology》2000,16(5):303-307
In automated manufacturing systems such as flexible manufacturing systems (FMSs), one of the most important issues is the
detection of tool wear during the cutting process. This paper presents a hybrid learning method to map the relationship between
the features of cutting vibration and the tool wear condition. The experimental results show that it can be used effectively
to monitor the tool wear in drilling. First, a neural network model with fuzzy logic (FNN), responding to learning algorithms,
is presented. It has many advantageous features, compared to a backpropagation neural network, such as less computation. Secondly,
the experimental results show that the frequency distribution of vibration changes as the tool wears, so the r.m.s. of the
different frequency bands measured indicates the tool wear condition. Finally, FNN is used to describe the relationship between
the characteristics of vibration and the tool wear condition. The experimental results demonstrate the feasibility of using
vibration signals to monitor the drill wear condition. 相似文献
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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
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Ning Fang P. Srinivasa Pai S. Mosquea 《The International Journal of Advanced Manufacturing Technology》2011,52(1-4):65-77
High-speed machining has been receiving growing attention and wide applications in modern manufacture. Extensive research has been conducted in the past on tool flank wear and crater wear in high-speed machining (such as milling, turning, and drilling). However, little study was performed on the tool edge wear??the wear of a tool cutting edge before it is fully worn away??that can result in early tool failure and deteriorated machined surface quality. The present study aims to fill this important research gap by investigating the effect of tool edge wear on the cutting forces and vibrations in 3D high-speed finish turning of nickel-based superalloy Inconel 718. A carefully designed set of turning experiments were performed with tool inserts that have different tool edge radii ranging from 2 to 62???m. The experimental results reveal that the tool edge profile dynamically changes across each point on the tool cutting edge in 3D high-speed turning. Tool edge wear increases as the tool edge radius increases. As tool edge wear dynamically develops during the cutting process, all the three components of the cutting forces (i.e., the cutting force, the feed force, and the passive force) increase. The cutting vibrations that accompany with dynamic tool edge wear were analyzed using both the traditional fast Fourier transform (FFT) technique and the modern discrete wavelet transform technique. The results show that, compared to the FFT, the discrete wavelet transform is more effective and advantageous in revealing the variation of the cutting vibrations across a wide range of frequency bands. The discrete wavelet transform also reveals that the vibration amplitude increases as the tool edge wear increases. The average energy of wavelet coefficients calculated from the cutting vibration signals can be employed to evaluate tool edge wear in turning with tool inserts that have different tool edge radii. 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