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Review Article

Non-destructive testing of metal-based additively manufactured parts and processes: a review

, , , &
Article: e2266658 | Received 18 Jul 2023, Accepted 16 Sep 2023, Published online: 11 Oct 2023

References

  • Brenner K, Ergun AS, Firouzi K, et al. Advances in capacitive micromachined ultrasonic transducers. Micromachines. 2019;10(2):152. doi: 10.3390/mi10020152.
  • DebRoy T, Wei HL, Zuback JS, et al. Additive manufacturing of metallic components–process, structure and properties. Prog Mater Sci. 2018;92:112–224. doi: 10.1016/j.pmatsci.2017.10.001.
  • Leung CLA, Marussi S, Atwood RC, et al. In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing. Nat Commun. 2018;9(1):1355. doi: 10.1038/s41467-018-03734-7.
  • Chen Y, Peng X, Kong L, et al. Defect inspection technologies for additive manufacturing. Int J Extreme Manuf. 2021;3(2):022002. doi: 10.1088/2631-7990/abe0d0.
  • Koester L, Taheri H, Bigelow T, et al. Nondestructive testing for metal parts fabricated using powder based additive manufacturing. Mater Eval. 2018;76:514–524.
  • Everton S, Dickens P, Tuck C, et al. Using laser ultrasound to detect subsurface defects in metal laser powder bed fusion components. Jom. 2018;70:378–383. doi: 10.1007/s11837-017-2661-7.
  • Liu ZZ, Han QQ, Zhang ZH, et al. Design of a novel crack-free precipitation-strengthened nickel-based superalloy and composites for laser powder bed fusion. Virtual Phys Prototyp. 2023;18(1):2224769. doi: 10.1080/17452759.2023.2224769.
  • Yu W, Xiao Z, Zhang X, et al. Processing and characterization of crack-free 7075 aluminum alloys with elemental Zr modification by laser powder bed fusion. Mater Sci Addit Manuf. 2022;1(1):4. doi: 10.18063/msam.v1i1.4.
  • Chauveau D. Review of NDT and process monitoring techniques usable to produce high-quality parts by welding or additive manufacturing. Weld World. 2018;62:1097–1118. doi: 10.1007/s40194-018-0609-3.
  • Chua ZY, Ahn IH, Moon SK. Process monitoring and inspection systems in metal additive manufacturing: status and applications. Int J Precis Eng Manuf Green Technol. 2017;4:235–245. doi: 10.1007/s40684-017-0029-7.
  • Everton SK, Hirsch M, Stravroulakis P, et al. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials. 2016;95:431–445.
  • Taheri H, Shoaib MRM, Koester LW, et al. Powder-based additive manufacturing-a review of types of defects, generation mechanisms, detection, property evaluation and metrology. Int J Addit Subtract Mater Manuf. 2017;1(2):172–209.
  • Mandache C. Overview of non-destructive evaluation techniques for metal-based additive manufacturing. Mater Sci Technol. 2019;35(9):1007–1015. doi: 10.1080/02670836.2019.1596370.
  • Sreeraj PR, Mishra SK, Singh PK, et al. A review on non-destructive evaluation and characterization of additively manufactured components. Prog Addit Manuf. 2022;7:225–248. doi: 10.1007/s40964-021-00227-w.
  • Ramírez IS, Márquez FPG, Papaelias M. Review on additive manufacturing and non-destructive testing. J Manuf Syst. 2023;66:260–286. doi: 10.1016/j.jmsy.2022.12.005.
  • Chen X, Kong F, Fu Y, et al. A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model. Int J Adv Manuf Technol. 2021;117:707–727. doi: 10.1007/s00170-021-07807-8.
  • Serrati DSM, Machado MA, Oliveira JP, et al. Non-destructive testing inspection for metal components produced using wire and arc additive manufacturing. Metals. 2023;13(4):648. doi: 10.3390/met13040648.
  • Shaloo M, Schnall M, Klein T, et al. A review of non-destructive testing (NDT) techniques for defect detection: application to fusion welding and future wire arc additive manufacturing processes. Materials. 2022;15(10):3697. doi: 10.3390/ma15103697.
  • Du Pleaais A, Yadroitsev I, Yadroitsava I, et al. X-ray microcomputed tomography in additive manufacturing: a review of the current technology and applications. 3D Print Addit Manuf. 2018;5(3):227–247. doi: 10.1089/3dp.2018.0060.
  • Li ZR, Wu DL, Zhou JF, et al. Advances in online detection technology for laser additive manufacturing: a review. 3D Print Addit Manuf. 2023;10(3):467–489. doi: 10.1089/3dp.2021.0049.
  • Honarvar F, Varvani-Farahani A. A review of ultrasonic testing applications in additive manufacturing: defect evaluation, material characterization, and process control. Ultrasonics. 2020;108:106227. doi: 10.1016/j.ultras.2020.106227.
  • Lu QY, Wong CH. Additive manufacturing process monitoring and control by non-destructive testing techniques: challenges and in-process monitoring. Virtual Phys Prototyp. 2018;13(2):39–48. doi: 10.1080/17452759.2017.1351201.
  • ISO/ASTM 52900:2021. Standard terminology for additive manufacturing -- general principles -- terminology. West Conshohocken (PA, USA): ASTM International; 2021.
  • Bankong B, Abioye T, Olugbade T, et al. Review of post-processing methods for high-quality wire arc additive manufacturing. J Appl Psychol. 2023;39(2):129–146.
  • Gong X, Zeng D, Groeneveld-Meijer W, et al. Additive manufacturing: a machine learning model of process-structure-property linkages for machining behavior of Ti–6Al–4V. Mater Sci Add Manuf. 2022;1(6):1–16.
  • Savinov R, Shi J. Microstructure, mechanical properties, and corrosion performance of additively manufactured CoCrFeMnNi high-entropy alloy before and after heat treatment. Mater Sci Addit Manuf. 2023;2(1):42. doi: 10.36922/msam.42.
  • Sing SL, Huang S, Yeong WY. Effect of solution heat treatment on microstructure and mechanical properties of laser powder bed fusion produced cobalt-28chromium-6molybdenum. Mater Sci Eng A. 2020;769:138511. doi: 10.1016/j.msea.2019.138511.
  • Xue P, Zhu L, Xu P, et al. Effect of heat treatment on microstructure and mechanical properties of in-situ synthesized Ni2CrCoNb0. 16 multi-principal element alloy manufactured by directed energy deposition. Mater Sci Eng A. 2023;862:144398. doi: 10.1016/j.msea.2022.144398.
  • Chen H, Meng X, Chen J, et al. Wire-based friction stir additive manufacturing. Addit Manuf. 2023;70:103557.
  • Kumar S, Kar A. A review of solid-state additive manufacturing processes. Trans Indian Natl Acad Eng. 2021;6(4):955–973. doi: 10.1007/s41403-021-00270-7.
  • Li W, Yang K, Yin S, et al. Solid-state additive manufacturing and repairing by cold spraying: a review. J Mater Sci Technol. 2018;34(3):440–457. doi: 10.1016/j.jmst.2017.09.015.
  • Liu M, Kumar A, Bukkapatnam S, et al. A review of the anomalies in directed energy deposition (DED) processes & potential solutions-part quality & defects. Procedia Manuf. 2021;53:507–518. doi: 10.1016/j.promfg.2021.06.093.
  • Ngo TD, Kashani A, Imbalzano G, et al. Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos Part B Eng. 2018;143:172–196. doi: 10.1016/j.compositesb.2018.02.012.
  • Svetlizky D, Zheng B, Vyatskikh A, et al. Laser-based directed energy deposition (DED-LB) of advanced materials. Mater Sci Eng A. 2022;840:142967. doi: 10.1016/j.msea.2022.142967.
  • Tuncer N, Bose A. Solid-state metal additive manufacturing: a review. Jom. 2020;72(9):3090–3111. doi: 10.1007/s11837-020-04260-y.
  • Xie R, Shi Y, Liu H, et al. A novel friction and rolling based solid-state additive manufacturing method: microstructure and mechanical properties evaluation. Mater Today Commun. 2021;29:103005. doi: 10.1016/j.mtcomm.2021.103005.
  • Svetlizky D, Das M, Zheng B, et al. Directed energy deposition (DED) additive manufacturing: physical characteristics, defects, challenges and applications. Mater Today. 2021;49:271–295. doi: 10.1016/j.mattod.2021.03.020.
  • Wong KV, Hernandez A. A review of additive manufacturing. Int Sch Res Not. 2012;2012:208760.
  • Chua CK, Leong KF. 3D printing and additive manufacturing: principles and applications (with companion media pack)-of rapid prototyping. Singapore: World Scientific Publishing Company; 2014.
  • Alhijaily A, Kilic ZM, Bartolo A. Teams of robots in additive manufacturing: a review. Virtual Phys Prototyp. 2023;18:e2162929. doi: 10.1080/17452759.2022.2162929.
  • Safeea M, Bearee R, Neto P. An integrated framework for collaborative robot-assisted additive manufacturing. J Appl Psychol. 2019;81:406–413.
  • Gibson I, Rosen DW, Stucker B, et al. Additive manufacturing technologies. Vol. 17, New York: Springer; 2021.
  • Yang L, Hsu K, Baughman B, et al. Additive manufacturing of metals: the technology, materials, design and production. Cham: Springer; 2017.
  • Sing SL, Kuo C, Shih C, et al. Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing. Virtual Phys Prototyp. 2021;16(3):372–386. doi: 10.1080/17452759.2021.1944229.
  • Dass A, Moridi A. State of the art in directed energy deposition: from additive manufacturing to materials design. Coatings. 2019;9(7):418. doi: 10.3390/coatings9070418.
  • Sing SL, Huang S, Goh GD, et al. Emerging metallic systems for additive manufacturing: in-situ alloying and multi-metal processing in laser powder bed fusion. Prog Mater Sci. 2021;119:100795. doi: 10.1016/j.pmatsci.2021.100795.
  • Wang D, Liu L, Deng G, et al. Recent progress on additive manufacturing of multi-material structures with laser powder bed fusion. Virtual Phys Prototyp. 2022;17(2):329–365. doi: 10.1080/17452759.2022.2028343.
  • Nurhudan AI, Supriadi S, Whulanza Y, et al. Additive manufacturing of metallic based on extrusion process: a review. J Manuf Process. 2021;66:228–237. doi: 10.1016/j.jmapro.2021.04.018.
  • Yu W, Sing SL, Chua CK, et al. Influence of re-melting on surface roughness and porosity of AlSi10Mg parts fabricated by selective laser melting. J Alloys Compd. 2019;792:574–581. doi: 10.1016/j.jallcom.2019.04.017.
  • Sames WJ, List FA, Pannala S, et al. The metallurgy and processing science of metal additive manufacturing. Int Mater Rev. 2016;61(5):315–360. doi: 10.1080/09506608.2015.1116649.
  • He Y, Li M, Meng Z, et al. An overview of acoustic emission inspection and monitoring technology in the key components of renewable energy systems. Mech Syst Signal Process. 2021;148:107146. doi: 10.1016/j.ymssp.2020.107146.
  • Wolf SJ, Wu H, Parab N, et al. In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing. Sci Rep. 2019;9:962. doi: 10.1038/s41598-018-36678-5.
  • Aiden AM, Nicholas PC, Saad AK, et al. Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nat Commun. 2019;10:1987. doi: 10.1038/s41467-019-10009-2.
  • Khairallah SA, Anderson AT, Rubenchik A, et al. Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater. 2016;108:36–45. doi: 10.1016/j.actamat.2016.02.014.
  • Witkin DB, Sitzman S, Kim Y, et al. Experimental nondestructive characterization of an aluminum alloy prepared by powder-bed additive manufacturing. Mater Eval. 2018;76(4):489–502.
  • Brennan MC, Keist JS, Palmer TA. Defects in metal additive manufacturing processes. J Mater Eng Perform. 2021;30:4808–4818. doi: 10.1007/s11665-021-05919-6.
  • Manvatkar V, De A, DebRoy T. Heat transfer and material flow during laser assisted multi-layer additive manufacturing. J Appl Phys. 2014;116(2):124905. doi: 10.1063/1.4896751.
  • Cerniglia D, Scafidi M, Pantano A, et al. Inspection of additive-manufactured layered components. Ultrasonics. 2015;62:292–298. doi: 10.1016/j.ultras.2015.06.001.
  • DePond PJ, Guss G, Ly S, et al. In situ measurements of layer roughness during laser powder bed fusion additive manufacturing using low coherence scanning interferometry. Mater Des. 2018;154:347–359. doi: 10.1016/j.matdes.2018.05.050.
  • Acevedo R, Sedlak P, Kolman R, et al. Residual stress analysis of additive manufacturing of metallic parts using ultrasonic waves: state of the art review. J Mater Res Technol. 2020;9:9457–9477. doi: 10.1016/j.jmrt.2020.05.092.
  • Bartlett J, Li X. An overview of residual stresses in metal powder bed fusion. Addit Manuf. 2019;27:131–149.
  • Nadimpalli VK, Karthik G, Janakiram G, et al. Monitoring and repair of defects in ultrasonic additive manufacturing. Int J Adv Manuf Technol. 2020;108:1793–1810. doi: 10.1007/s00170-020-05457-w.
  • Gorelik M. Additive manufacturing in the context of structural integrity. Int J Fatigue. 2017;94:168–177. doi: 10.1016/j.ijfatigue.2016.07.005.
  • Ji L, Lu J, Tang S, et al. Research on mechanisms and controlling methods of macro defects in TC4 alloy fabricated by wire additive manufacturing. Materials. 2018;11:1104. doi: 10.3390/ma11071104.
  • Madhvacharyula AS, Pavan AVS, Gorthi S, et al. In situ detection of welding defects: a review. Weld World. 2022;66:611–628. doi: 10.1007/s40194-021-01229-6.
  • Dwivedi SK, Vishwakarma M, Soni A. Advances and researches on non destructive testing: a review. Mater Today Proc. 2018;5(2):3690–3698. doi: 10.1016/j.matpr.2017.11.620.
  • Zhang Y, Fuh JYH, Ye D, et al. In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches. J Appl Psychol. 2019;25:263–274.
  • Kanko JA, Sibley AP, Fraser JM. In situ morphology-based defect detection of selective laser melting through inline coherent imaging. J Mater Process Technol. 2016;231:488–500. doi: 10.1016/j.jmatprotec.2015.12.024.
  • Nair A, Cai CS. Acoustic emission monitoring of bridges: review and case studies. Eng Struct. 2010;32(6):1704–1714. doi: 10.1016/j.engstruct.2010.02.020.
  • Abdelrahman M, Reutzel E, Nassar AR, et al. Flaw detection in powder bed fusion using optical imaging. Addit Manuf. 2017;15:1–11.
  • Lu QY, Nguyen N, Hum A, et al. Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density. J Mater Process Technol. 2019;271:520–531. doi: 10.1016/j.jmatprotec.2019.04.026.
  • Lu QY, Nguyen NV, Hum AJ, et al. Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography. Addit Manuf. 2020;35:101287.
  • Hossain MS, Taheri H. In situ process monitoring for additive manufacturing through acoustic techniques. J Mater Eng Perform. 2020;29(10):6249–6262. doi: 10.1007/s11665-020-05125-w.
  • Biswal R, Zhang X, Shamir M, et al. Interrupted fatigue testing with periodic tomography to monitor porosity defects in wire+ arc additive manufactured Ti–6Al–4V. Addit Manuf. 2019;28:517–527.
  • Hassen AA, Kirka MM. Additive manufacturing: the rise of a technology and the need for quality control and inspection techniques. Mater Eval. 2018;76(4):438–453.
  • Sun W, Symes DR, Brenner CM, et al. Review of high energy x-ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components. Rep Prog Phys. 2022;85(1):016102. doi: 10.1088/1361-6633/ac43f6.
  • Beutel J, Kundel HL, Metter RLV. Handbook of medical imaging (physics and psychophysics). Vol. 1, Bellingham (WA): SPIE Optical Engineering Press. 2000; p. 949.
  • Khosravani MR, Reinicke T. On the use of X-ray computed tomography in assessment of 3D-printed components. J Nondestruct Eval. 2020;39:1–17. doi: 10.1007/s10921-020-00721-1.
  • Lu QY, Wong CH. Applications of non-destructive testing techniques for post-process control of additively manufactured parts. Virtual Phys Prototyp. 2017;12(4):301–321. doi: 10.1080/17452759.2017.1357319.
  • Malej S, Matjaz G, Crtomir D, et al. Hybrid additive manufacturing of Ti6Al4V with powder-bed fusion and direct-energy deposition. Mater Sci Eng. 2023;878:145229. doi: 10.1016/j.msea.2023.145229.
  • Samei J, Amirmaleki M, Dastgiri MS, et al. In-situ X-ray tomography analysis of the evolution of pores during deformation of AlSi10Mg fabricated by selective laser melting. Mater Lett. 2019;255:126512. doi: 10.1016/j.matlet.2019.126512.
  • Sanaei N, Fatemi A, Phan N. Defect characteristics and analysis of their variability in metal L-PBF additive manufacturing. Mater Des. 2019;182:108091. doi: 10.1016/j.matdes.2019.108091.
  • Siddique S, Imran M, Rauer M, et al. Computed tomography for characterization of fatigue performance of selective laser melted parts. Mater Des. 2015;83:661–669. doi: 10.1016/j.matdes.2015.06.063.
  • Smith TR, Sugar JD, Schoenung JM, et al. Relationship between manufacturing defects and fatigue properties of additive manufactured austenitic stainless steel. Mater Sci Eng A. 2019;765:138268. doi: 10.1016/j.msea.2019.138268.
  • Tammas-Williams S, Zhao H, Léonard F, et al. XCT analysis of the influence of melt strategies on defect population in Ti–6Al–4V components manufactured by selective electron beam melting. J Appl Psychol. 2015;102:47–61.
  • Thompson A, Maskery I, Leach RK. X-ray computed tomography for additive manufacturing: a review. Meas Sci Technol. 2016;27(7):072001. doi: 10.1088/0957-0233/27/7/072001.
  • Slotwinski JA, Garboczi EJ, Hebenstreit KM. Porosity measurements and analysis for metal additive manufacturing process control. J Res Natl Inst Stand Technol. 2014;119:494. doi: 10.6028/jres.119.019.
  • Kim FH, Moylan SP, Garboczi EJ, et al. Investigation of pore structure in cobalt chrome additively manufactured parts using X-ray computed tomography and three-dimensional image analysis. Addit Manuf. 2017;17:23–38.
  • Zhang M, Sun C, Zhang X, et al. Fatigue and fracture behaviour of laser powder bed fusion stainless steel 316L: influence of processing parameters. Mater Sci Eng A. 2017;703:251–261. doi: 10.1016/j.msea.2017.07.071.
  • Choo H, Sham K, Bohling J, et al. Effect of laser power on defect, texture, and microstructure of a laser powder bed fusion processed 316L stainless steel. Mater Des. 2019;164:107534. doi: 10.1016/j.matdes.2018.12.006.
  • Baruchel J, Buffiere J, Cloetens P, et al. Advances in synchrotron radiation microtomography. Scr Mater. 2006;55(1):41–46. doi: 10.1016/j.scriptamat.2006.02.012.
  • Kastner J, Harrer B, Requena G, et al. A comparative study of high resolution cone beam X-ray tomography and synchrotron tomography applied to Fe-and Al-alloys. NDT E Int. 2010;43(7):599–605. doi: 10.1016/j.ndteint.2010.06.004.
  • Stock SR. Recent advances in X-ray microtomography applied to materials. Int Mater Rev. 2008;53(3):129–181. doi: 10.1179/174328008X277803.
  • Carlton H, Haboub A, Gallegos G, et al. Damage evolution and failure mechanisms in additively manufactured stainless steel. Mater Sci Eng A. 2016;651:406–414. doi: 10.1016/j.msea.2015.10.073.
  • Pandiyan V, Drissi-Daoudi R, Shevchik S, et al. Analysis of time, frequency and time-frequency domain features from acoustic emissions during laser powder-bed fusion process. Procedia CIRP. 2020;94:392–397. doi: 10.1016/j.procir.2020.09.152.
  • Eschner N, Weiser L, Häfner B, et al. Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions. Addit Manuf. 2020;34:101324.
  • Lédeczi H, Hay T, Volgyesi P, et al. Wireless acoustic emission sensor network for structural monitoring. IEEE Sens J. 2009;11(9):1370–1377. doi: 10.1109/JSEN.2009.2019315.
  • Zahedi F, Huang H. A wireless acoustic emission sensor remotely powered by light. Smart Mater Struct. 2014;23(3):035003. doi: 10.1088/0964-1726/23/3/035003.
  • Ito K, Kusano M, Demura M, et al. Detection and location of microdefects during selective laser melting by wireless acoustic emission measurement. Addit Manuf. 2021;40:101915.
  • Barile C, Casavola C, Pappalettera G, et al. Acoustic emission signal processing for the assessment of corrosion behaviour in additively manufactured. Mech Mater. 2022;170:104347. doi: 10.1016/j.mechmat.2022.104347.
  • Liu P, Sohn H, Kundu T, et al. Noncontact detection of fatigue cracks by laser nonlinear wave modulation spectroscopy (LNWMS). NDT E Int. 2014;66:106–116. doi: 10.1016/j.ndteint.2014.06.002.
  • Ramalho A, Santos TG, Bevans B, et al. Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel. Addit Manuf. 2022;51:102585.
  • Strantza M, Van Hemelrijck D, Guillaume P, et al. Acoustic emission monitoring of crack propagation in additively manufactured and conventional titanium components. Mech Res Commun. 2017;84:8–13. doi: 10.1016/j.mechrescom.2017.05.009.
  • Barile C, Casavola C, Pappalettera G, et al. Acoustic emission descriptors for the mechanical behavior of selective laser melted samples: an innovative approach. Mech Mater. 2020;148:103448. doi: 10.1016/j.mechmat.2020.103448.
  • Liu P, Yi K, Sohn H. Estimation of silicon wafer coating thickness using ultrasound generated by femtosecond laser. J Nondestruct Eval Diagn Progn Eng Syst. 2021;4(1):011005.
  • Scruby CB. Some applications of laser ultrasound. Ultrasonics. 1989;27(4):195–209. doi: 10.1016/0041-624X(89)90043-7.
  • Selim H, Delgado-Prieto M, Trull J, et al. Defect reconstruction by non-destructive testing with laser induced ultrasonic detection. Ultrasonics. 2020;101:106000. doi: 10.1016/j.ultras.2019.106000.
  • Hu S, Shi W, Lu C, et al. Rapid detection of cracks in the rail foot by ultrasonic B-scan imaging using a shear horizontal guided wave electromagnetic acoustic transducer. NDT E Int. 2021;120:102437. doi: 10.1016/j.ndteint.2021.102437.
  • Derusova DA, Vavilov VP, Druzhinin N, et al. Investigating vibration characteristics of magnetostrictive transducers for air-coupled ultrasonic NDT of composites. NDT E Int. 2019;107:102151. doi: 10.1016/j.ndteint.2019.102151.
  • Kim YY, Kwon YE. Review of magnetostrictive patch transducers and applications in ultrasonic nondestructive testing of waveguides. Ultrasonics. 2015;62:3–19. doi: 10.1016/j.ultras.2015.05.015.
  • Lee SE, Liu P, Ko YW, et al. Study on effect of laser-induced ablation for Lamb waves in a thin plate. Ultrasonics. 2019;91:121–128. doi: 10.1016/j.ultras.2018.07.019.
  • Chimenti DE. Review of air-coupled ultrasonic materials characterization. Ultrasonics. 2014;54(7):1804–1816. doi: 10.1016/j.ultras.2014.02.006.
  • Zimermann R, Mohseni E, Lines D, et al. Multi-layer ultrasonic imaging of as-built Wire+ Arc additive manufactured components. Addit Manuf. 2021;48:102398.
  • Nadimpalli VK, Yang L, Nagy PB. In-situ interfacial quality assessment of ultrasonic additive manufacturing components using ultrasonic NDE. NDT E Int. 2018;93:117–130. doi: 10.1016/j.ndteint.2017.10.004.
  • Rieder H, Spies M, Bamberg J, et al. On-and offline ultrasonic characterization of components built by SLM additive manufacturing. AIP Conf Proc. 2016;1706(1):130002. doi: 10.1063/1.4940605.
  • Lopez AB, Santos J, Sousa J, et al. Phased array ultrasonic inspection of metal additive manufacturing parts. J Nondestruct Eval. 2019;38:1–11. doi: 10.1007/s10921-018-0529-6.
  • Javadi Y, MacLeod CN, Pierce SG, et al. Ultrasonic phased array inspection of a Wire+ Arc Additive Manufactured (WAAM) sample with intentionally embedded defects. Addit Manuf. 2019;29:100806.
  • Chabot A, Laroche N, Carcreff E, et al. Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing. J Intell Manuf. 2020;31(5):1191–1201. doi: 10.1007/s10845-019-01505-9.
  • Rao J, Yang J, He J, et al. Elastic least-squares reverse-time migration with density variation for flaw imaging in heterogeneous structures. Smart Mater Struct. 2020;29(3):035017. doi: 10.1088/1361-665X/ab6ba4.
  • Rao J, Sing SL, Lim JCW, et al. Detection and characterisation of defects in directed energy deposited multi-material components using full waveform inversion and reverse time migration. Virtual Phys Prototyp. 2022;17(4):1047–1057. doi: 10.1080/17452759.2022.2086142.
  • Lv G, Yao Z, Chen D, et al. Fast and high-resolution laser-ultrasonic imaging for visualizing subsurface defects in additive manufacturing components. Mater Des. 2023;225:111454. doi: 10.1016/j.matdes.2022.111454.
  • Chen Y, Jiang L, Peng Y, et al. Ultra-fast laser ultrasonic imaging method for online inspection of metal additive manufacturing. Opt Lasers Eng. 2023;160:107244. doi: 10.1016/j.optlaseng.2022.107244.
  • Liu S, Jia K, Wan H, et al. Inspection of the internal defects with different size in Ni and Ti additive manufactured components using laser ultrasonic technology. Opt Laser Technol. 2022;146:107543. doi: 10.1016/j.optlastec.2021.107543.
  • Zhang J, Wu J, Zhao X, et al. Laser ultrasonic imaging for defect detection on metal additive manufacturing components with rough surfaces. Appl Opt. 2020;59(33):10380–10388. doi: 10.1364/AO.405284.
  • Holland SD, Reusser RS. Material evaluation by infrared thermography. Annu Rev Mater Res. 2016;46:287–303. doi: 10.1146/matsci.2016.46.issue-1.
  • Shrestha R, Kim W. Evaluation of coating thickness by thermal wave imaging: a comparative study of pulsed and lock-in infrared thermography–part I: simulation. Infrared Phys Technol. 2017;83:124–131. doi: 10.1016/j.infrared.2017.04.016.
  • Liu T, Lough CS, Sehhat H, et al. In-situ infrared thermographic inspection for local powder layer thickness measurement in laser powder bed fusion. Addit Manuf. 2022;55:102873.
  • Ciampa F, Mahmoodi P, Pinto F, et al. Recent advances in active infrared thermography for non-destructive testing of aerospace components. Sensors. 2018;18(2):609. doi: 10.3390/s18020609.
  • Sreedhar U, Krishnamurthy CV, Balasubramaniam K, et al. Automatic defect identification using thermal image analysis for online weld quality monitoring. J Mater Process Technol. 2012;212(7):1557–1566. doi: 10.1016/j.jmatprotec.2012.03.002.
  • Machado MA, Silva MI, Martins AP, et al. Double active transient thermography. NDT E Int. 2022;125:102566. doi: 10.1016/j.ndteint.2021.102566.
  • Silva HV, Martins AP, Machado MA, et al. Double active thermographic inspection of additive manufacturing composites: numerical modelling and validation. Measurement. 2023;218:113212. doi: 10.1016/j.measurement.2023.113212.
  • Hwang S, Kim H, Lim HJ, et al. Automated visualization of steel structure coating thickness using line laser scanning thermography. Autom Constr. 2022;139:104267. doi: 10.1016/j.autcon.2022.104267.
  • Vavilov VP, Burleigh DD. Review of pulsed thermal NDT: physical principles, theory and data processing. NDT E Int. 2015;73:28–52. doi: 10.1016/j.ndteint.2015.03.003.
  • Zhao Y, Tinsley L, Addepalli S, et al. A coefficient clustering analysis for damage assessment of composites based on pulsed thermographic inspection. NDT E Int. 2016;83:59–67. doi: 10.1016/j.ndteint.2016.06.003.
  • Ciampa F, Scarselli G, Meo M. On the generation of nonlinear damage resonance intermodulation for elastic wave spectroscopy. J Acoust Soc Am. 2017;141(4):2364–2374. doi: 10.1121/1.4979256.
  • Guo X. Ultrasonic infrared thermography of aluminium thin plates for crack inspection in friction stir welded joints. IEEE Sens J. 2020;20(12):6524–6531. doi: 10.1109/JSEN.7361.
  • Du B, He Y, He Y, et al. Intelligent classification of silicon photovoltaic cell defects based on eddy current thermography and convolution neural network. IEEE Trans Ind Inform. 2019;16(10):6242–6251. doi: 10.1109/TII.9424.
  • Zu R, Yang Y, Huang X, et al. A stress detection method for metal components based on eddy current thermography. J Appl Psychol. 2023;133:102762.
  • Mirala A, Al Qaseer MT, Donnell KM. Health monitoring of RAM-coated structures by active microwave thermography. IEEE Trans Instrum Meas. 2021;70:6005411. doi: 10.1109/TIM.19.
  • Zou X, Mirala A, Sneed L, et al. Detection of CFRP-concrete interfacial debonding using active microwave thermography. Compos Struct. 2021;260:113261. doi: 10.1016/j.compstruct.2020.113261.
  • Fierro GPM, Flora F, Boccaccio M, et al. Real-time automated composite scanning using forced cooling infrared thermography. Infrared Phys Technol. 2021;118:103860. doi: 10.1016/j.infrared.2021.103860.
  • Lei L, Ferrarini G, Bortolin A, et al. Thermography is cool: defect detection using liquid nitrogen as a stimulus. NDT E Int. 2019;102:137–143. doi: 10.1016/j.ndteint.2018.11.012.
  • Szymanik B, Chady T, Goracy K. Numerical modelling and experimental evaluation of the composites using active infrared thermography with forced cooling. Quant Infrared Thermogr J. 2020;17(2):107–129. doi: 10.1080/17686733.2019.1625243.
  • Mireles J, Ridwan S, Morton PA, et al. Analysis and correction of defects within parts fabricated using powder bed fusion technology. Surf Topogr Metrol Prop. 2015;3(3):034002. doi: 10.1088/2051-672X/3/3/034002.
  • Kolb CG, Zier K, Grager J, et al. An investigation on the suitability of modern nondestructive testing methods for the inspection of specimens manufactured by laser powder bed fusion. SN Appl Sci. 2021;3:1–16. doi: 10.1007/s42452-021-04685-3.
  • Cerniglia D, Montinaro N. Defect detection in additively manufactured components: laser ultrasound and laser thermography comparison. Procedia Struct Integr. 2016;8:154–162. doi: 10.1016/j.prostr.2017.12.016.
  • Montinaro N, Cerniglia D, Pitarresi G. Defect detection in additively manufactured titanium prosthesis by flying laser scanning thermography. Procedia Struct Integr. 2018;12:165–172. doi: 10.1016/j.prostr.2018.11.098.
  • Fu Y, Downey AR, Yuan L, et al. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J Manuf Process. 2022;75:693–710. doi: 10.1016/j.jmapro.2021.12.061.
  • Cui W, Zhang Y, Zhang X, et al. Metal additive manufacturing parts inspection using convolutional neural network. Appl Sci. 2020;10(2):545. doi: 10.3390/app10020545.
  • Zhang Y, Soon HG, Ye D, et al. Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks. IEEE Trans Ind Inform. 2019;16(9):5769–5779. doi: 10.1109/TII.9424.
  • Garland A, White B, Jared B, et al. Deep convolutional neural networks as a rapid screening tool for complex additively manufactured structures. Addit Manuf. 2020;35:101217.
  • Delli U, Chang S. Automated process monitoring in 3D printing using supervised machine learning. Procedia Manuf. 2018;26:865–870. doi: 10.1016/j.promfg.2018.07.111.
  • Caggiano A, Zhang J, Alfieri V, et al. Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann. 2019;68(1):451–454. doi: 10.1016/j.cirp.2019.03.021.
  • Huang Z, Wang J, Fu X, et al. DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection. Inf Sci. 2020;522:241–258. doi: 10.1016/j.ins.2020.02.067.
  • Redmon J, Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 7263–7271.
  • Li W, Zhang H, Wang G, et al. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot Comput Integr Manuf. 2023;80:102470. doi: 10.1016/j.rcim.2022.102470.
  • Snell R, Tammas-Williams S, Chechik L, et al. Methods for rapid pore classification in metal additive manufacturing. Jom. 2020;72:101–109. doi: 10.1007/s11837-019-03761-9.
  • Montazeri M, Nassar AR, Stutzman CB, et al. Heterogeneous sensor-based condition monitoring in directed energy deposition. Addit Manuf. 2019;30:100916.
  • Gobert C, Kudzal A, Sietins J, et al. Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning. Addit Manuf. 2020;36:101460.
  • Wong V, Ferguson M, Law KH, et al. Segmentation of additive manufacturing defects using U-net. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; Vol. 85376; 2021. V002T02A029.
  • Yang H, Wang W, Li C, et al. Deep learning-based X-ray computed tomography image reconstruction and prediction of compression behavior of 3D printed lattice structures. Addit Manuf. 2022;54:102774.
  • Hossain MS, Taheri H. In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network (CNN). Int J Adv Manuf Technol. 2021;116:3473–3488. doi: 10.1007/s00170-021-07721-z.
  • Shevchik SA, Masinelli G, Kenel C, et al. Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Trans Ind Inform. 2019;15(9):5194–5203. doi: 10.1109/TII.9424.
  • Ye D, Hong GS, Zhang Y, et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int J Adv Manuf Technol. 2018;96:2791–2801. doi: 10.1007/s00170-018-1728-0.
  • Gaja H, Liou F. Defects monitoring of laser metal deposition using acoustic emission sensor. Int J Adv Manuf Technol. 2017;90:561–574. doi: 10.1007/s00170-016-9366-x.
  • Gaja H, Liou F. Defect classification of laser metal deposition using logistic regression and artificial neural networks for pattern recognition. Int J Adv Manuf Technol. 2018;94:315–326. doi: 10.1007/s00170-017-0878-9.
  • Mohammadi MG, Mahmoud D, Elbestawi M. On the application of machine learning for defect detection in L-PBF additive manufacturing. Opt Laser Technol. 2021;143:107338. doi: 10.1016/j.optlastec.2021.107338.
  • Park S, Hong J, Ha T, et al. Deep learning-based ultrasonic testing to evaluate the porosity of additively manufactured parts with rough surfaces. Metals. 2021;11(2):290. doi: 10.3390/met11020290.
  • Park SH, Choi S, Jhang KY. Porosity evaluation of additively manufactured components using deep learning-based ultrasonic nondestructive testing. Int J Precis Eng Manuf Green Technol. 2021;9:395–407. doi: 10.1007/s40684-021-00319-6.
  • Rao J, Yang F, Mo H, et al. Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion. J Sound Vib. 2023;542:117418. doi: 10.1016/j.jsv.2022.117418.
  • Xu W, Zhang J, Li X, et al. Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface. NDT E Int. 2022;125:102548. doi: 10.1016/j.ndteint.2021.102548.
  • Zhang X, Saniie J, Heifetz A. Detection of defects in additively manufactured stainless steel 316L with compact infrared camera and machine learning algorithms. J Appl Psychol. 2020;72(12):4244–4253.
  • Zhang X, Saniie J, Cleary W, et al. Quality control of additively manufactured metallic structures with machine learning of thermography images. JOM. 2020;72(12):4682–4694. doi: 10.1007/s11837-020-04408-w.
  • Baumgartl H, Tomas J, Buettner R, et al. A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Prog Addit Manuf. 2020;5(3):277–285. doi: 10.1007/s40964-019-00108-3.
  • Chen X, Zhang H, Hu J, et al. A passive on-line defect detection method for wire and arc additive manufacturing based on infrared thermography. 2019 International Solid Freeform Fabrication Symposium; 2019.
  • Jeon I, Yang L, Ryu K, et al. Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network. Addit Manuf. 2021;47:102295.
  • Gbenga EE. Using non-destructive testing for the manufacturing of composites for effective cost saving: a case study of a commercial prepreg CFC. Int J Mater Eng. 2016;6(2):28–38.
  • Lim HJ, Lee J, Sohn H. Nondestructive yield strength estimation for 3D-printed Ti–6Al–4V plates using Eddy-current measurement. Sens Smart Struct Technol Civ Mech Aerosp Syst. 2023;12486:39–45.
  • Spurek MA, Spierings AB, Lany M, et al. In-situ monitoring of powder bed fusion of metals using eddy current testing. Addit Manuf. 2022;60:103259.
  • Bento JB, Lopez A, Pires I, et al. Non-destructive testing for wire+ arc additive manufacturing of aluminium parts. Addit Manuf. 2019;29:100782.
  • Ehlers H, Pelkner M, Thewes R. Heterodyne eddy current testing using magnetoresistive sensors for additive manufacturing purposes. IEEE Sens J. 2020;20(11):5793–5800. doi: 10.1109/JSEN.7361.
  • Javier GM, Jaime G, Ernesto V. Non-destructive techniques based on Eddy current testing. Sensors. 2011;11:2525–2565. doi: 10.3390/s110302525.
  • Yang L, Sohn H, Ma Z, et al. Real-time layer height estimation during multi-layer directed energy deposition using domain adaptive neural networks. Comput Ind. 2023;148:103882. doi: 10.1016/j.compind.2023.103882.