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Rapid Identification of Defects in Metal Additive Manufacturing Components by LIBS Combined with BP-Neural Network and Random Forest Method
Abstract
Rapid detection of defects in metal additive manufacturing (AM) components remains a challenge. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was used to establish a rapid identification of metal AM defects and defect-free control groups. The corresponding spectral acquisition of metal AM components with defects and without defects was carried out, and the research elements (Fe, Cr, Mn, and Ti) and corresponding spectral lines were obtained in combination with the NIST database. The spectral lines with features of importance greater than the average value are selected by random forest (RF). The selected spectral lines were used as the input variables of the k-nearest neighbor (KNN) model and the backpropagation neural network (BPNN) model. The classification performance and verification results of KNN, RF-KNN, and RF-BPNN models were compared. The results showed that the RF-BPNN model exhibited the best accuracy, sensitivity, and specificity in the training set, test set and validation set, with accuracies of 99.4, 97.2, and 96.67%, respectively. This indicates that LIBS combined with RF-BPNN can be used for the detection of defects in metal AM components.
Supporting agencies: This paper was supported by the National Natural Science Foundation of China (NSFC: 62405029), the Department of Science and Technology of Jilin Province of China (grant No. 20240302043GX), and the Department of Science and Technology of Jilin Province of China (grant No. YDZJ202401296ZYTS)
About the Authors
S. Gao
Quanzhou University of Information Engineering
China
China
Shanping Gao
Quanzhou, Fujian
X. Lin
Quanzhou University of Information Engineering; Changchun University of Technology
China
China
Xiaomei Lin
Quanzhou, Fujian; Changchun, Jilin
Y. Huang
Quanzhou University of Information Engineering
China
China
Yixiang Huang
Quanzhou, Fujian
Z. Chen
Quanzhou University of Information Engineering
China
China
Zongxu Chen
Quanzhou, Fujian
H. Chen
Quanzhou University of Information Engineering
China
China
Huijin Chen
Quanzhou, Fujian
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Review
For citations:
Gao S., Lin X., Huang Y., Chen Z., Chen H. Rapid Identification of Defects in Metal Additive Manufacturing Components by LIBS Combined with BP-Neural Network and Random Forest Method. Zhurnal Prikladnoii Spektroskopii. 2025;92(3):414.
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