文献类型：期刊文献

英文题名：Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition

作者：Wang, Haijie[1];Li, Bo[1];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[3]Shanghai Collaborat Innovat Ctr High End Equipmen, Shanghai 200237, Peoples R China

年份：2022

卷号：122

期号：5-6

起止页码：2277

外文期刊名：INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

收录：;EI(收录号：20223612680767);WOS:【SCI-EXPANDED(收录号:WOS:000849302900004)】；

基金：This research work is sponsored by the National Natural Science Foundation of China (No. 52175140, No. 51835003), International Collaboration Program from Science and Technology Commission of Shanghai Municipality in China (No. 19110712500), Natural Science Foundation of Shanghai in China (No. 20ZR1414000), and the Fundamental Research Funds for the Central Universities in China.

语种：英文

外文关键词：Acoustic emission monitoring; Variational mode decomposition; Selective laser melting; Machine learning

摘要：Selective laser melting (SLM) additive manufacturing overcomes the geometric limits of complex components produced with traditional subtractive methods, which has significant advantages in designing and manufacturing special-shaped components. However, due to the lack of adequate and effective process monitoring, it is difficult to ensure the reliability of as-built parts and the stability of the additive manufacturing process. Therefore, it is necessary to monitor the as-built part quality of the SLM process. An in situ quality monitoring method of acoustic emission (AE) based on machine learning and improved variational modal decomposition (VMD) is proposed in the present work. The VMD parameters are adjusted based on the whale optimization algorithm (WOA) and average energy entropy to realize the adaptive decomposition of the AE signals. Each sub-mode is evaluated according to the signal energy, the feature vector used for SLM printing quality prediction is extracted. Finally, the artificial neural network (ANN) and support vector machine (SVM) are employed for quality prediction. The improved VMD method is compared with empirical modal decomposition (EMD), aiming to verify the predictive validity of printing quality in the SLM process. The results show that predicting SLM printing quality based on improved VMD is better than the EMD method. Meanwhile, it is verified that online monitoring of SLM for improving printing quality can be achieved based on the AE technique.