APPLIED LASER, Volume. 45, Issue 1, 26(2025)

A Defect Identification and Classification Method for Powder Bed Based on Defect Feature Extraction

Feng Jingwei1,2, Xing Fei1,2、*, Bian Hongyou1, and Miao Liguo1,2
Author Affiliations
  • 1College of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China
  • 2Nanjing Zhongke Yuchen Laser Technology Co., Ltd., Nanjing 210038, Jiangsu, China
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    References(14)

    [1] [1] HUANG Y, LEU M C, MAZUMDER J, et al. Additive manufacturing: Current state, future potential, gaps and needs, and recommendations[J]. Journal of Manufacturing Science and Engineering, 2015, 137(1): 014001.

    [3] [3] OMIYALE B O, OLUGBADE T O, ABIOYE T E, et al. Wire arc additive manufacturing of aluminium alloys for aerospace and automotive applications: A review[J]. Materials Science and Technology, 2022, 38(7): 391-408.

    [4] [4] MOSTAFAEI A, ZHAO C, HE Y N, et al. Defects and anomalies in powder bed fusion metal additive manufacturing[J]. Current Opinion in Solid State and Materials Science, 2022, 26(2): 100974.

    [5] [5] GALY C, LE GUEN E, LACOSTE E, et al. Main defects observed in aluminum alloy parts produced by SLM: From causes to consequences[J]. Additive Manufacturing, 2018, 22: 165-175.

    [6] [6] GRASSO M, REMANI A, DICKINS A, et al. In-situ measurement and monitoring methods for metal powder bed fusion: An updated review[J]. Measurement Science and Technology, 2021, 32(11): 112001.

    [7] [7] HERZOG T, BRANDT M, TRINCHI A, et al. Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing[J]. Journal of Intelligent Manufacturing, 2024, 35(4): 1407-1437.

    [10] [10] CAGGIANO A, ZHANG J J, ALFIERI V, et al. Machine learning-based image processing for on-line defect recognition in additive manufacturing[J]. CIRP Annals, 2019, 68(1): 451-454.

    [11] [11] SCIME L, BEUTH J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm[J]. Additive Manufacturing, 2018, 19: 114-126.

    [12] [12] SCIME L, BEUTH J. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing, 2018, 24: 273-286.

    [14] [14] TAN PHUC L, SEITA M. A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing[J]. Materials & Design, 2019, 164: 107562.

    [15] [15] LE T P, WANG X G, DAVIDSON K P, et al. Experimental analysis of powder layer quality as a function of feedstock and recoating strategies[J]. Additive Manufacturing, 2021, 39: 101890.

    [16] [16] FISCHER F G, ZIMMERMANN M G, PRAETZSCH N, et al. Monitoring of the powder bed quality in metal additive manufacturing using deep transfer learning[J]. Materials & Design, 2022, 222: 111029.

    [17] [17] LIN Z Q, LAI Y W, PAN T T, et al. A new method for automatic detection of defects in selective laser melting based on machine vision[J]. Materials, 2021, 14(15): 4175.

    [19] [19] HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621.

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    Feng Jingwei, Xing Fei, Bian Hongyou, Miao Liguo. A Defect Identification and Classification Method for Powder Bed Based on Defect Feature Extraction[J]. APPLIED LASER, 2025, 45(1): 26

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    Paper Information

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    Received: Jun. 8, 2023

    Accepted: Apr. 17, 2025

    Published Online: Apr. 17, 2025

    The Author Email: Xing Fei (xingfei@raycham.com)

    DOI:10.14128/j.cnki.al.20254501.026

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