基于声发射的磨削表面粗糙度模型及实验验证

Model and experimental verification of grinding surface roughness based on acoustic emission

为实现对磨削过程中表面粗糙度的预测,在磨削过程中增加声发射装置,采用AE信号监测磨削状态,分析AE信号特征参量和频谱随磨削深度a_(p)、砂轮速度vs和进给速度v_(w)等磨削参数变化的规律。结果表明:随着a_(p)和v_(w)的增大,AE信号特征参量的有效值和振铃计数值都增大,AE信号的主要能量集中频谱在90~140 kHz,对应的频谱幅值呈逐渐增大趋势;而随着vs逐渐增大,AE信号特征参量的有效值逐渐减小,振铃计数值逐渐增大,频段对应的频谱幅值呈逐渐减小的趋势。对数据进一步分析,得出AE信号特征参量与加工表面粗糙度的对应关系,为表面粗糙度预测模型建立提供样本。利用基于BP神经网络的多信息融合算法对AE信号的多种特征参量信息进行合理融合,建立基于AE信号的磨削加工表面粗糙度多信息融合预测模型,该模型可在实际生产中预测磨削表面粗糙度。

To predict the surface roughness in the grinding process,an acoustic emission device(AE)was incorporated into the grinding process to monitor the grinding state using AE signals.The variations in AE signal characteristic parameters and frequency spectrum with respect to grinding parameters,such as grinding depth a_(p),grinding wheel speed vs and feed speed v_(w)were analyzed.The results show that as a_(p)and v_(w)increase,the effective values and ringing count values of the AE signal's characteristic parameters both increase.The main energy concentration spectrum of the AE signal is between 90 and 140 kHz,and the corresponding spectrum amplitude shows a gradual increasing trend.With the gradual increase of vs,the effective value of AE signal characteristic parameters gradually decreases,the ringing count value gradually increases,and the spectral amplitude corresponding to the frequency band shows a gradual decreasing trend.Further data analysis reveals the corresponding relationship between AE signal characteristic parameters and machining surface roughness,providing a sample for establishing a surface roughness prediction model.The multi-information fusion algorithm,based on a BP neural network,is used to reasonably fuse various characteristic parameters of the AE signal.And the multi-information fusion prediction model for grinding surface roughness based on AE signal was established.After experimental verification,this model can predict the roughness of the ground surface in actual production.