激光选区熔化过程声发射信号的降噪与分类预测方法

Signal Denoising and Classification Prediction Method for On-line Monitoring of Acoustic Emission During Laser melting Process

激光选区熔化(Selective laser melting,SLM)中激光工艺参数显著影响成形件质量,而现行的工艺优化路径大多是基于激光能量密度经验性公式的“盲摸”工艺试验,难以全面反映影响成形件质量的诸多环境因素,且在SLM过程中,熔池失稳、激光辐照不连续的情况时有发生,因此亟需开发SLM过程中工艺参数在线动态调控及质量监测技术,以保障制造工艺的可靠性和成形件质量的稳定性。通过声发射信号处理与神经网络算法,实现对采用不同激光工艺参数的熔道SLM成形过程的声发射信号分类预测。在降噪模块中,为确定经验模态分解的分解层数从而将其作为神经网络输入神经元的数量,提出一种基于序列间相关性和反双曲正切函数的直观评判方法。在分类预测模块中,对比基于经验模态分解和小波包变换两种声信号特征提取方法所设计的神经网络性能。研究结果表明神经网络模型对于不同激光参数产生的声发射信号进行分类预测的可行性,从而可直接指导SLM工艺参数的在线调控优化或优选,并为SLM过程的成形质量在线监测系统奠定理论方法基础。

Laser process parameters in selective laser melting(SLM)significantly affect the quality of formed parts.Most of the current process optimization paths are"blind touch"process tests based on the empirical formula of laser energy density,which is difficult to fully reflect the many environmental factors that affect the quality of the formed parts,and in the SLM process,the instability of the molten pool and the discontinuous laser irradiation occur from time to time.Therefore,the online dynamic control of process parameters and quality monitoring technology in the SLM process needs to be developed urgently to ensure the reliability of the manufacturing process and the stability of the quality of the formed parts.Through signal processing and neural network algorithm,the classification of acoustic emission signals in the SLM forming process of the melt channel with different laser process parameters is realized.In the noise reduction module,an intuitive evaluation method based on the correlation between sequences and the inverse hyperbolic tangent function is proposed to integrate the two algorithms of empirical mode decomposition and neural network.In the classification prediction module,the performance of the neural network designed based on empirical mode decomposition and wavelet packet transform for two acoustic signal feature extraction methods is compared.The feasibility of the neural network model to classify and predict the acoustic emission signals generated by different laser parameters has been verified,which can directly guide the online control optimization or optimization of the SLM process parameters,and lay the theoretical method foundation for the online monitoring system of the forming quality of the SLM process.