基于PSO-BP单晶金刚石刀具刃磨方向多信息融合在线识别
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1.长春工业大学 电气与电子工程学院;2.中国科学院长春光学精密机械与物理研究所

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TP273

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吉林省科技发展计划项目(编号:20210201104GX);国家自然科学(62075216)


Online recognition of single crystal diamond tool grinding direction based on PSO-BP and multi-information fusion
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Changchun University of Technology

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    摘要:

    为了提高单晶金刚石刀具刃磨方向在线识别精度,以及解决刃磨监测中单一传感器采集信息有限的问题,提出一种基于多信息融合与粒子群优化(particle swarm optimization,PSO)算法优化BP神经网络的单晶金刚石刀具刃磨方向在线识别方法。通过采集刃磨过程中的振动信号和声发射(acoustic emission,AE)信号,采用小波包分解法分析刀具振动信号,得出与刀具刃磨方向强相关的特征频段,采用参数分析法来分析声发射信号,得出特征参数。将振动信号特征频段能量值和声发射信号特征参数作为识别刀具刃磨方向的特征参量。将特征参量作为BP神经网络模型输入进行融合,在线识别刀具刃磨方向。针对BP 神经网络的容易陷入局部最小值的缺点,利用PSO算法优化神经网络权值和阈值,有效解决陷入局部最小值的问题。实验结果表明,经PSO-BP与多信息融合对单晶金刚石刀具刃磨方向在线识别准确率得到了有效提高,达到85%的准确率,为单晶金刚石刀具刃磨方向在线识别提供了一种新方法。

    Abstract:

    In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and address the limitation of acquiring limited information from a single sensor in grinding monitoring, this study proposes a method for online recognition of the grinding direction of single crystal diamond tools based on multi-information fusion and particle swarm optimization (PSO) algorithm for optimizing the BP neural network. Vibration signals and acoustic emission (AE) signals were collected during the grinding process. The wavelet packet decomposition method was applied to analyze the vibration signals of the tool and identify the characteristic frequency bands strongly correlated with the grinding direction. The parameter analysis method was used to analyze the AE signals and extract the characteristic parameters. The energy values of the characteristic frequency bands in the vibration signals and the characteristic parameters of the AE signals were taken as the feature parameters for identifying the grinding direction of the tool. These feature parameters were then used as inputs to the BP neural network model for fusion and online recognition of the grinding direction. To overcome the disadvantage of the BP neural network easily getting stuck in local minima, the PSO algorithm was utilized to optimize the weights and thresholds of the neural network, effectively solving the problem of local minima. Experimental results demonstrated that the PSO-BP neural network combined with multi-information fusion significantly improved the online recognition accuracy of the grinding direction of single crystal diamond tools, achieving an accuracy of 85%. This proposed method provides a new approach for online recognition of the grinding direction of single crystal diamond tools.

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冯雪雯,赵彬,马海涛,等. 基于PSO-BP单晶金刚石刀具刃磨方向多信息融合在线识别[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-25
  • 最后修改日期:2024-07-01
  • 录用日期:2024-07-09
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