一种基于优化变分模态分解的微细磨削非稳态特征识别方法

本发明提供了一种基于优化变分模态分解的微细磨削非稳态特征识别方法，步骤如下：首先沿微结构微细磨削预路径中均匀设置声发射信号采样区间，顺序采集声发射信号后，以峰值为中心截取信号流；随后以排列熵、最大中心频率、模态数为约束条件迭代求解变分模态分解算法的最优模态数K，并分解信号流；根据非稳态特征模拟试验提取并叠加微磨具磨损、材料崩边、亚表面损伤深度、截形误差等非稳态特征相关分量；最后以重构信号能量占比与非稳态特征的相关系数判据及重构信号分量的分布概率判据选择信号识别方式，实现非稳态特征的快速识别。本发明有效地实现微细磨削非稳态特征的准确识别，为微细磨削过程控制提供精确的识别数据。 The inven...

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Bibliographic Details
Format Patent
LanguageChinese
Published 28.05.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:本发明提供了一种基于优化变分模态分解的微细磨削非稳态特征识别方法，步骤如下：首先沿微结构微细磨削预路径中均匀设置声发射信号采样区间，顺序采集声发射信号后，以峰值为中心截取信号流；随后以排列熵、最大中心频率、模态数为约束条件迭代求解变分模态分解算法的最优模态数K，并分解信号流；根据非稳态特征模拟试验提取并叠加微磨具磨损、材料崩边、亚表面损伤深度、截形误差等非稳态特征相关分量；最后以重构信号能量占比与非稳态特征的相关系数判据及重构信号分量的分布概率判据选择信号识别方式，实现非稳态特征的快速识别。本发明有效地实现微细磨削非稳态特征的准确识别，为微细磨削过程控制提供精确的识别数据。 The invention provides a micro-grinding unsteady state feature recognition method based on optimized variational mode decomposition, which comprises the following steps of: firstly, uniformly setting acoustic emission signal sampling intervals in a micro-structure micro-grinding pre-path, sequentially acquiring acoustic emission signals, and intercepting a signal flow by taking a peak value as a center; iteratively solving the optimal modal number K of a variational modal decomposition algorithm by taking permutation entropy, the maximum center frequency and the modal number as constraint conditions, and decomposing a signal flow; unsteady-state feature related components such as micro-grinding tool abrasion, material edge
Bibliography:Application Number: CN202310380758