journal article·2021-04-01Open access

Defect inspection technologies for additive manufacturing

Yao Chen, Xing Peng, Lingbao Kong, Guangxi Dong, Afaf Remani, Richard Leach

Abstract

Abstract Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Industrial Vision Systems and Defect Detection · Additive Manufacturing and 3D Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (100+)

Show source-provided references
1. Application of 3D printing technology for designing light-weight unmanned aerial vehicle wing structures10.1007/s40684-014-0028-x ↗
2. Metal additive manufacturing: a review10.1007/s11665-014-0958-z ↗
3. Monitoring of laser metal-wire additive manufacturing temperature field using infrared thermography10.3788/IRLA201847.0604002 ↗
4. Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies10.1016/j.phpro.2010.08.089 ↗
5. Titanium/hydroxyapatite (Ti/HA) gradient materials with quasi-continuous ratios fabricated by SLM: material interface and fracture toughness10.1016/j.matdes.2017.12.037 ↗
6. Standard Terminology for Additive Manufacturing Technologies
7. Multiple material additive manufacturing-part 1: a review10.1080/17452759.2013.778175 ↗
8. Geometrical metrology for metal additive manufacturing10.1016/j.cirp.2019.05.004 ↗
9. Feedback control of layerwise laser melting using optical sensors10.1016/j.phpro.2010.08.078 ↗
10. Microstructure and mechanical properties of TA15-Ti2AlNb bimetallic structures by laser additive manufacturing10.1016/j.msea.2020.140019 ↗
11. Processing parameters in laser powder bed fusion metal additive manufacturing10.1016/j.matdes.2020.108762 ↗
12. Feedback control of selective laser melting
13. Process defects and in situ monitoring methods in metal powder bed fusion: a review10.1088/1361-6501/aa5c4f ↗
14. Non-destructive quality control methods in additive manufacturing: a survey10.1108/RPJ-08-2019-0224 ↗
15. An overview of Direct Laser Deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics10.1016/j.addma.2015.07.001 ↗
16. Investigation of heating behavior of laser beam on powder stream in directed energy deposition10.1016/j.surfcoat.2020.126061 ↗
17. Surface roughness and morphology customization of additive manufactured open porous Ti6Al4V structures10.3390/ma6104737 ↗
18. A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness10.1108/RPJ-02-2013-0017 ↗
19. Metal additive manufacturing in the commercial aviation industry: a review10.1016/j.jmsy.2019.08.005 ↗
20. The effect of powder oxidation on defect formation in laser additive manufacturing10.1016/j.actamat.2018.12.027 ↗
21. Defect detection in additively manufactured components: laser ultrasound and laser thermography comparison10.1016/j.prostr.2017.12.016 ↗
22. Additive manufacturing in radiation oncology: a review of clinical practice, emerging trends and research opportunities10.1088/2631-7990/ab70af ↗
23. Performance characterization of laser powder bed fusion fabricated Inconel 718 treated with experimental hot isostatic processing cycles10.3390/jmmp4030073 ↗
24. Search for the optimal build direction in additive manufacturing technologies: a review10.3390/jmmp4030071 ↗
25. A preliminary study of in-situ defects measurement for additive manufacturing based on multi-spectrum10.1117/12.2504800 ↗
26. Additive manufacturing technology: the status, applications, and prospects10.1007/s00170-018-1932-y ↗
27. Invited review article: review of post-process optical form metrology for industrial-grade metal additive manufactured parts10.1063/1.4944983 ↗
28. Defect detection using the phased-array laser ultrasonic crack diffraction enhancement method10.1016/j.optcom.2020.126070 ↗
29. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning10.1016/j.autcon.2020.103291 ↗
30. Identifying flow defects in amorphous alloys using machine learning outlier detection methods10.1016/j.scriptamat.2020.05.038 ↗
31. Defect detection in additive manufacturing via a toolpath overlaid melt-pool-temperature tomography10.2351/7.0000055 ↗
32. Research on measurement of the molten pool image during laser cladding process
33. Estimation of laser solid forming process based on temperature measurement10.1016/j.optlastec.2009.04.016 ↗
34. A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring10.1007/s40964-019-00108-3 ↗
35. Non-destructive testing application of radiography and ultrasound for wire and arc additive manufacturing10.1016/j.addma.2018.03.020 ↗
36. Online monitoring of additive manufacturing processes using ultrasound
37. Inspection of additive-manufactured layered components10.1016/j.ultras.2015.06.001 ↗
38. Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks10.1016/j.infrared.2020.103334 ↗
39. Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing10.1115/1.4037891 ↗
40. In-process monitoring of selective laser melting: spatial detection of defects via image data analysis10.1115/1.4034715 ↗
41. Flaw detection in powder bed fusion using optical imaging10.1016/j.addma.2017.02.001 ↗
42. Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review10.1007/s00170-017-1172-6 ↗
43. Research status of defects and defect treatment technology for laser additive manufactured products10.3788/LOP56.100004 ↗
44. Reduction of micro-cracking in nickel superalloys processed by selective laser melting: a fundamental alloy design approach10.1016/j.actamat.2015.04.035 ↗
45. Carter · 2012 · 577
46. The influence of the laser scan strategy on grain structure and cracking behaviour in SLM powder-bed fabricated nickel superalloy10.1016/j.jallcom.2014.06.172 ↗
47. The liquation cracking behavior of IN738LC superalloy during low power Nd:YAG pulsed laser welding10.1016/j.matchar.2012.02.019 ↗
48. Intergranular liquation cracking in heat affected zone of a welded nickel based superalloy in as cast condition10.1179/174328407X213323 ↗
49. Numerical analysis of the weldability of superalloys10.1016/S1359-6454(00)00361-X ↗
50. Investigation of remelting and preheating in SLM of 18Ni300 maraging steel as corrective and preventive measures for porosity reduction10.1007/s00170-017-0697-z ↗
51. Densification behavior, microstructure evolution, and wear performance of selective laser melting processed commercially pure titanium10.1016/j.actamat.2012.04.006 ↗
52. Boundary liquation and interface cracking characterization in laser deposition of Inconel 738 on directionally solidified Ni-based superalloy10.1016/j.scriptamat.2005.03.047 ↗
53. Residual stresses in selective laser sintering and selective laser melting10.1108/13552540610707013 ↗
54. Selective laser melting of iron-based powder10.1016/j.jmatprotec.2003.11.051 ↗
55. Evaluation of the residual stresses in metallic materials produced by additive manufacturing technology: effect of microstructure10.1016/j.coche.2019.12.004 ↗
56. Residual stress via the contour method in compact tension specimens produced via selective laser melting10.1016/j.scriptamat.2014.05.016 ↗
57. Quality performance evaluation of thin walled PLA 3D printed parts using the Taguchi method and grey relational analysis10.3390/jmmp4020047 ↗
58. Methods and techniques for improving microstructure and performance of metal additively manufactured materials10.16080/j.issn1671-833x.2018.10.040 ↗
59. Electron beam additive manufacturing of titanium components: properties and performance10.1115/1.4025773 ↗
60. Influences of energy density on porosity and microstructure of selective laser melted 17-4PH stainless steel
61. Formation and reduction of hydrogen porosity during selective laser melting of AlSi10Mg10.1016/j.jmatprotec.2015.02.013 ↗
62. A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing10.1016/j.addma.2020.101399 ↗
63. The role of microstructure and local crystallographic orientation near porosity defects on the high cycle fatigue life of an additive manufactured Ti-6Al-4V10.1016/j.matchar.2020.110576 ↗
64. Fatigue life of additively manufactured Ti–6Al–4V in the very high cycle fatigue regime10.1016/j.ijfatigue.2016.05.018 ↗
65. Elucidation of bubble evolution and defect formation in directed energy deposition based on direct observation10.1016/j.addma.2019.101026 ↗
66. Evolution of porosity and geometrical quality through the ceramic extrusion additive manufacturing process stages10.1016/j.addma.2020.101038 ↗
67. Laser metal deposition for additive manufacturing of AA5024 and nanoparticulate TiC modified AA5024 alloy composites prepared with balling milling process10.1016/j.optlastec.2020.106438 ↗
68. Balling behavior of stainless steel and nickel powder during selective laser melting process10.1007/s00170-011-3566-1 ↗
69. Balling processes during selective laser treatment of powders10.1108/13552540410526953 ↗
70. Balling phenomenon and cracks in alumina ceramics prepared by direct selective laser melting assisted with pressure treatment10.1016/j.ceramint.2020.02.178 ↗
71. Defect detection in additively manufactured lattices
72. Defect detection in additively manufactured titanium prosthesis by flying laser scanning thermography10.1016/j.prostr.2018.11.098 ↗
73. Defect formation mechanisms in selective laser melting: a review10.1007/s10033-017-0121-5 ↗
74. A review of ultrasonic testing applications in additive manufacturing: defect evaluation, material characterization, and process control10.1016/j.ultras.2020.106227 ↗
75. In situ flaw detection by IR-imaging during electron beam melting10.1108/13552541211231572 ↗
76. In situ defect detection in selective laser melting via full-field infrared thermography
77. Combined detection technology for micro leakage of titanium/steel and zirconium/steel clad plate vessel
78. Summary of NDE of additive manufacturing efforts in NASA10.1063/1.4914594 ↗
79. Eddy current testing of deep-lying defects in paramagnetic metals by flux-gate sensor
80. Eddy current detection of subsurface defects for additive/subtractive hybrid manufacturing10.1007/s00170-017-1354-2 ↗
81. New progress of the study and application of advanced ultrasonic testing technology10.3901/JME.2017.22.001 ↗
82. Air-coupling ultrasonic testing of defects in LY12 duralumin alloys
83. Development of laser ultrasonics inspection for online monitoring of additive manufacturing10.1007/s40194-018-0567-9 ↗
84. Real-time recognition method for 0.8 cm darning needles and KR22 bearings based on convolution neural networks and data increase10.3390/app8101857 ↗
85. Convolutional neural network-based embarrassing situation detection under camera for social robot in smart homes10.3390/s18051530 ↗
86. Solar cell surface defect detection based on visual saliency
87. Wire defect recognition of spring-wire socket using multitask convolutional neural networks10.1109/TCPMT.2018.2794540 ↗
88. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques10.1016/j.autcon.2018.08.006 ↗
89. Method of improved deep wavelet auto-encoder in bearing fault diagnosis10.3778/j.issn.1002-8331.1811-0372 ↗
90. Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer10.3390/a11030028 ↗
91. Detection of a casting defect tracked by deep convolution neural network10.1007/s00170-018-1894-0 ↗
92. Vision-based defect detection for mobile phone cover glass using deep neural networks10.1007/s12541-018-0096-x ↗
93. Classification and recognition method of sheet metal parts surface defects based on convolution neural network
94. Multi-kernel fuzzy clustering based on auto-encoder for fMRI functional network10.1016/j.eswa.2020.113513 ↗
95. Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network10.1016/j.asoc.2020.106333 ↗
96. A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis10.1016/j.ymssp.2019.106608 ↗
97. Deep residual networks with a fully connected reconstruction layer for single image super-resolution10.1016/j.neucom.2020.04.030 ↗
98. Fault diagnosis of rolling bearing based on deep residual shrinkage network
99. Scale insensitive and focus driven mobile screen defect detection in industry10.1016/j.neucom.2018.03.013 ↗
100. Fault prediction research of high voltage circuit breakers based on LSTM recurrent neural network optimized by CAS