摘要
机械加工过程中的刀具磨损状态对零件的加工质量、生产效率和成本影响极大。对刀具磨损的采集信号进行合理准确地降噪处理,是实现刀具磨损检测的核心技术。利用熵值法构造出信噪比、平滑度、均方根误差3个传统评价指标的权重,提出降噪质量的融合评价指标。对比仿真信号的去噪结果与真实信号发现,融合评价指标更具可行性和优越性。以最大融合评价指标为目标,提出降噪参数的优化方法。针对小波阈值去噪后的实际刀具磨损振动信号,与传统的单个评价指标相比,融合评价指标优选出来的降噪参数,不仅能够去除[6 kHz,12 kHz]高频部分的噪声信号,而且能够比较完整地保留[0 kHz,6 kHz]低频部分的真实信号。通过提取出刀具磨损特征值,建立切削工艺参数与刀具磨损之间的神经网络预测模型。刀具磨损试验结果表明,预测结果与试验测量值之间的最大误差不超出6.0%,进一步验证了基于多指标融合评价的最佳降噪参数能够准确地提取出刀具磨损信号的特征量。
Tool wear has a great influence on the machining quality,production efficiency and cost of parts in the process of machining.The reasonable and accurate noise reduction for collected tool wear signal is the core technology for tool wear detection.Based on the weights of the signal-to-noise ratio,the smoothness and the mean square root error which are constructed by the entropy method,the fusion evaluation index of noise reduction quality is proposed.Comparison of the denoised simulated signal with the real signal shows that the fusion evaluation index is of the feasibility and superiority.And then an optimization method of noise reduction parameters is presented with the objective of maximum fusion evaluation index.For the actual vibration signal of tool wear after wavelet threshold denoising,the noise reduction parameters optimized by the fusion evaluation index proposed in this paper can be used to remove the noise signals at high frequency[6 kHz,12 kHz]and also retain completely the real signals at low frequency[0 kHz,6 kHz]compared with the traditional single evaluation index.Finally,a neural network prediction model is established from the extracted feature values of tool wear for describing the relationship between the tool wear and the cutting parameters.Experimental results show that the tool wear signal features can be accurately extracted using the optimal noise reduction parameters based on multi-index fusion evaluation.The maximum error between the predicted values and the measured values is no more than 6.0%.
作者
秦国华
高杰
叶海潮
姜国杰
黄帅
赖晓春
QIN Guohua;GAO Jie;YE Haichao;JIANG Guojie;HUANG Shuai;LAI Xiaochuun(School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University,Nanchang 330063,Jiangxi,China;AECC Beijing Insititute of Aeronautical Materials,Beijing 100095,China;Jiangxi Education International Cooperation and Teacher Development Center,Jiangxi Provincial Department of Education,Nanchang 330083,Jiangxi,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2021年第9期2013-2023,共11页
Acta Armamentarii
基金
国家自然科学基金项目(51765047)
江西省主要学科学术和技术带头人计划项目(20172BCB22013)
江西省科技厅重点研发计划项目(20203BBE53049)。
关键词
刀具磨损
评价指标
熵值法
小波阈值去噪
降噪参数
tool wear
noise reduction parameter
entropy method
wavelet threshold denoising
evaluation index
