In-Situ Quality Intelligent Classification of Additively Manufactured Parts Using a Multi-Sensor Fusion Based Melt Pool Monitoring System  

作　　者：Qianru Wu Fan Yang Cuimeng Lv Changmeng Liu Wenlai Tang Jiquan Yang 

机构地区：[1]Jiangsu Key Laboratory of 3D Printing Equipment&Manufacturing,School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing,210023,China [2]School of Mechanical Engineering,Beijing Institute of Technology,Beijing,100081,China

出　　处：《Additive Manufacturing Frontiers》2024年第3期74-86,共13页增材制造前沿(英文)

基　　金：supported by Key Research and Development Pro-gram of Jiangsu Province(Grant Nos.BE2022069-1 and BE2022069-2);Natural Science Research Project of Jiangsu Higher Education Institu-tions(Grant Nos.22KJB460030 and 22KJB460004);Suzhou Science and Technology Development Plan(Grant No.SYC2022020);startup fund-ing at the Nanjing Normal University(Grant No.184080H202B318);2022 Nanjing Carbon Peak and Neutrality Technology Innovation Special Fund(Grant No.202211017).

摘　　要：Although laser powder bed fusion(LPBF)technology is considered one of the most promising additive man-ufacturing techniques,the fabricated parts still suffer from porosity defects,which can severely impact their mechanical performance.Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status;thus,the monitoring accuracy can be improved.However,existing multi-sensing signals are mainly optical and acoustic,and camera-based signals are mostly layer-wise images captured after printing,preventing real-time monitoring.This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors.High-speed cameras,photodiodes,and microphones were used to collect signals during the experimental process.All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images,which enabled the capture of more localized features.Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory,single-sensor,dual-sensor and triple-sensor fusion monitoring models were in-vestigated with the three types of signals,and their performances were compared.The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy,with accuracy rates of 97.98%,92.63%,and 100%for high-,medium-,and low-quality samples,respectively.Hence,a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process,which has the potential to reduce porosity defects.Finally,the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.

关 键 词：Additive manufacturing In-situ quality classification Multi-sensor fusion Melt pool area Deep convolutional neural network Selective laser melting 

分 类 号：TG1[金属学及工艺—金属学]