导航

ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (9): 425855-425855.doi: 10.7527/S1000-6893.2021.25855

• Material Engineering and Mechanical Manufacturing • Previous Articles     Next Articles

Machine learning of emission intensity signal of laser powder bed fusion molten pool

DUAN Yucong1, WANG Xuede1, ZHOU Xin1, ZHANG Peiyu1, GUO Xiyang1, CHENG Xing1,2, FAN Junwei2   

  1. 1. Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi 'an 710038, China;
    2. Xi 'an Aerospace Mechatronics & Intelligent Manufacturing Co., Ltd, Xi 'an 710100, China
  • Received:2021-05-24 Revised:2021-06-10 Online:2022-09-15 Published:2021-08-03
  • Supported by:
    National Natural Science Foundation of China (91860136); Key Research and Development Plan of Guangdong Province (2018B090905001); Key Science and Technology Project of Shanxi Province (2018zdzx01-04-01)

Abstract: Process monitoring and quality identification of Powder Bed Fusion (PBF) are the key technologies to ensure its manufacturing quality. Among the monitoring signals, the molten pool radiation intensity signal contains rich molten pool characteristic information, but the direct connection between the monitoring signal and the material science phenomenon is not clear, and it is suitable to use machine learning algorithms to carry out in-depth research. Through 316L stainless steel forming experiments, different process parameters (partial deviation from the process window) are set, and the emission intensity signal of the molten pool is collected during the forming process. The signal data are preprocessed through data segmentation, feature extraction and feature selection, and a data set for machine learning is constructed. Twenty-one different machine learning algorithms are used. The molten pool emission intensity data are firstly classified according to the process parameters (such as high, medium, and low laser power). The trained algorithms can identify abnormalities (abnormal emission intensity, representative laser power and scanning speed deviate from the best state). Secondly, the emission intensity data of the molten pool are classified according to the quality (density, surface roughness) of the final formed block, and the quality can be identified in actual production through the trained algorithms. The results show that for the identification of abnormalities of the emission intensity of the molten pool, the classification effect of the secondary support vector machine algorithm is the best, with an accuracy of over 96.4%. For prediction of density and surface roughness, the prediction results show different distributions due to the complex relationship between the sample quality and the data sample, but the prediction accuracy has reached more than 96.0%.

Key words: machine learning, laser powder bed fusion, molten pool emission intensity, abnormality identification, quality identification

CLC Number: