文献类型：期刊文献

英文题名：Traditional machine learning and deep learning for predicting melt-pool cross-sectional morphology of laser powder bed fusion additive manufacturing with thermographic monitoring

作者：Wang, Haijie[1];Li, Bo[1,2,3];Zhang, Saifan[1];Xuan, Fuzhen[1,2]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Collaborat Innovat Ctr High End Equipment, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Addit Mfg & Intelligent Equipment Res Inst, Shanghai 200237, Peoples R China

年份：2025

卷号：36

期号：3

起止页码：2079

外文期刊名：JOURNAL OF INTELLIGENT MANUFACTURING

收录：;EI(收录号：20241315821792);WOS:【SCI-EXPANDED(收录号:WOS:001194603900001)】；

基金：This research work is sponsored by National Natural Science Foundation of China (Grant No. 52175140), National Key R&D Program of China (Grant No. 2022YFB4602102), Pre-research project of Civil Aerospace Technology (Grant No. D020301), Equipment Pre-research Sharing Technology Key Project (Grant No. JZX7Y20210422004601), and Fundamental Research Funds for the Central Universities in China (Grant No. JKG01231610).

语种：英文

外文关键词：Laser powder bed fusion; Machine learning; Deep learning; Near-infrared imaging; Melt pool; Additive manufacturing

摘要：The intricate non-equilibrium and rapid solidification behavior inherent in laser powder bed fusion (LPBF) additive manufacturing affects the quality and performance of as-built parts. To evaluate and predict the quality of LPBF-built parts, engaging in real-time monitoring of the LPBF process by leveraging thermal information derived from the melt pool becomes significant. In this work, the insights conveyed by near-infrared (NIR) thermal-imaging on melt pools during the LPBF process were explored, with the assistance of machine learning (ML) and deep learning (DL) methods, aiming to develop ML and DL models capable of recognizing NIR melt-pool monitoring images and predicting invisible geometries of laser-tracks. Traditional ML models, including support vector machines, were used to establish a non-linear mapping relationship between NIR thermal images and cross-sectional geometries of solidified laser-tracks. That was achieved by extracting melt-pool NIR image features based on prior knowledge while analyzing the influence of laser parameters on the melt pools. Then, DL models such as convolutional neural networks were improved to extract multi-scale features from the melt-pool thermal images through self-learning mechanisms. By comprehensively merging multi-scale features, these DL models effectively captured and reflected vital NIR image information from the melt pool. The various methodologies collectively provided real-time insights for monitoring and controlling the LPBF processes, thereby facilitating reasoning about and predicting imperceptible geometries of the cross-sectional solidified laser-tracks within the as-built parts.