Capability to detect and localize typical defects of laser powder bed fusion (L-PBF) process: an experimental investigation with different non-destructive techniques

被引:19
作者
D'Accardi, Ester [1 ]
Krankenhagen, Rainer [2 ]
Ulbricht, Alexander [2 ]
Pelkner, Matthias [2 ]
Pohl, Rainer [2 ]
Palumbo, Davide [1 ]
Galietti, Umberto [1 ]
机构
[1] Politecn Bari, Dipartimento Meccan Matemat & Management, Via Orabona 4, I-70125 Bari, Italy
[2] Bundesanstalt Mat Forsch & Prufung BAM, Unter Eichen 87, D-12205 Berlin, Germany
关键词
Typical defects in metal additive manufacturing (AM); Non-destructive techniques NDT; Thermographic testing (TT); Eddy current testing (ET); Micro-computed tomography (mu CT); Laser powder bed fusion (L-PBF) process; Keyhole and lack of fusion defects; PULSE-PHASE; THERMOGRAPHY; TOMOGRAPHY; COMPONENTS; ULTRASOUND; POROSITY;
D O I
10.1007/s40964-022-00297-4
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a mu CT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods.
引用
收藏
页码:1239 / 1256
页数:18
相关论文
共 40 条
  • [1] Defect detection in thick aircraft samples based on HTS SQUID-magnetometry and pattern recognition
    Allweins, K
    Gierelt, G
    Krause, H
    von Kreutzbruck, M
    [J]. IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2003, 13 (02) : 250 - 253
  • [2] Comparison of MWIR thermography and high-speed NIR thermography in a laser metal deposition (LMD) process
    Altenburg, S. J.
    Maierhofer, C.
    Strasse, A.
    Gumenyuk, A.
    [J]. 14TH QUANTITATIVE INFRARED THERMOGRAPHY CONFERENCE, 2018, : 136 - 140
  • [3] ASTM Committee F42 on Additive Manufacturing Technologies, 2012, STAND TERM ADD MAN T
  • [4] A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring
    Baumgartl, Hermann
    Tomas, Josef
    Buettner, Ricardo
    Merkel, Markus
    [J]. PROGRESS IN ADDITIVE MANUFACTURING, 2020, 5 (03) : 277 - 285
  • [5] Defect Detection in Additively Manufactured Components: Laser Ultrasound and Laser Thermography Comparison
    Cerniglia, Donatella
    Montinaro, Nicola
    [J]. AIAS2017 - 46TH CONFERENCE ON STRESS ANALYSIS AND MECHANICAL ENGINEERING DESIGN, 2018, 8 : 154 - 162
  • [6] Defect inspection technologies for additive manufacturing
    Chen, Yao
    Peng, Xing
    Kong, Lingbao
    Dong, Guangxi
    Remani, Afaf
    Leach, Richard
    [J]. INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING, 2021, 3 (02)
  • [7] Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data
    Coeck, Sam
    Bisht, Manisha
    Plas, Jan
    Verbist, Frederik
    [J]. ADDITIVE MANUFACTURING, 2019, 25 : 347 - 356
  • [8] Pulsed Phase Thermography Approach for the Characterization of Delaminations in CFRP and Comparison to Phased Array Ultrasonic Testing
    D'Accardi, E.
    Palano, F.
    Tamborrino, R.
    Palumbo, D.
    Tati, A.
    Terzi, R.
    Galietti, U.
    [J]. JOURNAL OF NONDESTRUCTIVE EVALUATION, 2019, 38 (01)
  • [9] D'Accardi E., 2019, PROC, V27, P24, DOI DOI 10.3390/PROCEEDINGS2019027024
  • [10] Capability of active thermography to detect and localize pores in Metal Additive Manufacturing materials
    D'Accardi, Ester
    Ulbricht, Alexander
    Krankenhagen, Rainer
    Palumbo, Davide
    Galietti, Umberto
    [J]. 49TH ITALIAN ASSOCIATION FOR STRESS ANALYSIS CONFERENCE (AIAS 2020), 2021, 1038