文献类型：期刊文献

英文题名：Machine learning-assisted acoustic emission monitoring for track formability prediction of laser powder bed fusion

作者：Wang, Haijie[1,2];Zhang, Saifan[1];Li, Bo[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, Addit Mfg & Intelligent Equipment Res Inst, Shanghai 200237, Peoples R China;[3]Shanghai Collaborat Innovat Ctr High end Equipment, Shanghai 200237, Peoples R China

年份：2024

卷号：38

外文期刊名：MATERIALS TODAY COMMUNICATIONS

收录：;EI(收录号：20241015708669);WOS:【SCI-EXPANDED(收录号:WOS:001203514000001)】；

基金：This research work is sponsored by National Natural Science Foundation of China (Grant No. 52175140) , Natural Science Foundation of Shanghai Municipality (Grant No. 20ZR1414000) , and International Collaboration Program from Science and Technology Commission of Shanghai Municipality (Grant No. 19110712500) in China.

语种：英文

外文关键词：Machine learning; Acoustic emission; Laser powder bed fusion; Online monitoring

摘要：Laser powder bed fusion (L-PBF) additive manufacturing offers many advantages in directly fabricating geometrically complex parts. To achieve the optimal L-PBF-built part quality, the laser process parameters should be tailored before building or real-time adjusted during building if necessary. Monitoring the formation quality of L-PBF-built metal tracks during the building process is a technical prerequisite. The work studies the acoustic emission (AE) monitoring and machine learning (ML) to predict the formation quality of as-built tracks. The AE signals from the L-PBF processes under different laser parameters are collected. A method of deep neural network-based denoising is employed to identify and eliminate the AE noise-hits. Signal segments of interest signals in the track melting-solidification stage are detected and extracted. The features for constructing the ML models are extracted by combining wavelet packet transform and self-organization map network. The random forest is used for formation quality prediction of the as-built tracks. The correlation between AE signals and asbuilt track quality is analyzed based on the similarity matrix of self-organization map. The results indicate that the as-built track quality can be monitored by AE technology and ML model. It contributes to that the L-PBF process stability and the as-built track quality reliability can be ensured by adjusting the process parameters in time during the L-PBF process.