Skip to main content
SpringerLink
Log in

A review of the parameter-signature-quality correlations through in situ sensing in laser metal additive manufacturing

  • Critical Review
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Laser metal additive manufacturing (LMAM) is a significant part of the advanced manufacturing industry. The technology enables the production of complex parts via layer-by-layer deposition through robust bonding mechanisms. Despite the inherent advantages of additive technology, the quality control of its products remains the challenge to increase the applicability of the AM parts and products as complex physical processes occur at the melt pool (MP) where laser and metal interact. There are many variables in LMAM that impact the final part quality, and many scholars have investigated how the individual variable affects the part quality. The contribution this review makes is to assess how process parameters affect process signatures (derived from in situ techniques) and how these in turn affect part quality. By focussing on the connection across process parameters, process signatures, and part quality, the review aims to systematically tackle the complexity of the LMAM process and thus provide insight into online diagnostic of quality estimation. This paper starts with common part quality issues and briefly reviews process parameters to part quality correlations. As a vital step to link the process parameters and part quality, different in situ monitoring techniques and their acquired data are summarised. The correlations between the important factors affecting the quality of the additive process are systematically reviewed. These correlations include parameter-signature, signature-quality, and parameter-signature-quality correlations. Linking process parameters and part quality through the process signatures is highly desirable. These correlations bring opportunities to design a more comprehensive feedback controller to improve the repeatability and reliability of LMAM systems. Results found through the investigation of disconnected factors contribute to building a holistic understanding of LMAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

ANN:

Artificial neural network

BP:

Backpropagation

CAD:

Computer-aided design

CCD:

Charge-coupled device

CMOS:

Complementary metal–oxide–semiconductor

CNN:

Convolutional neural network

CT:

Computed tomography

DBN:

Deep belief network

DSLR:

Digital single-lens reflex

FEM:

Finite element method

FVM:

Finite volume method

GA:

Genetic algorithm

HIP:

Hot isostatic pressing

ILC:

Iterative learning control

IR:

Infrared

KNN:

K-nearest neighbours

LMAM:

Laser metal additive manufacturing

LMD:

Laser metal deposition

LS-SVM:

Least square support vector machine

LWIR:

Long-wave infrared

MIMO:

Multi-input-multi-output

ML:

Machine learning

MP:

Melt pool

MWIR:

Middle-wave infrared

NDT:

Non-destructive testing

NIR:

Near-infrared

NN:

Neural network

PCA:

Principal component analysis

PSO:

Particle swarm optimization

SISO:

Single-input–single-output

SLM:

Selective laser melting

SLS:

Selective laser sintering

SOM:

Self-organizing map

SWIR:

Short-wave infrared

SVM:

Support vector machine

SVR:

Support vector regression

UTS:

Ultimate tensile strength

VIS:

Visible

YS:

Yield strength

References

  1. Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Des 95:431–445. https://doi.org/10.1016/j.matdes.2016.01.099

    Article  Google Scholar 

  2. Lu QY, Wong CH (2017) Additive manufacturing process monitoring and control by non-destructive testing techniques: challenges and in-process monitoring. Virtual Phys Prototyp 13(2):39–48. https://doi.org/10.1080/17452759.2017.1351201

    Article  Google Scholar 

  3. Kanko JA, Sibley AP, Fraser JM (2016) In situ morphology-based defect detection of selective laser melting through inline coherent imaging. J Mater Process Technol 231:488–500. https://doi.org/10.1016/j.jmatprotec.2015.12.024

    Article  Google Scholar 

  4. Thompson SM, Bian L, Shamsaei N, Yadollahi A (2015) An overview of Direct Laser Deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics. Addit Manuf 8:36–62. https://doi.org/10.1016/j.addma.2015.07.001

    Article  Google Scholar 

  5. Pratheesh Kumar S, Elangovan S, Mohanraj R, Ramakrishna JR (2021) Review on the evolution and technology of state-of-the-art metal additive manufacturing processes. Mater Today: Proc 46:7907–7920. https://doi.org/10.1016/j.matpr.2021.02.567

    Article  Google Scholar 

  6. Bhavar V, Kattire P, Patil V, Khot S, Gujar K, Singh R (2017) A review on powder bed fusion technology of metal additive manufacturing. In: Valencia VV, Badiru AB, Liu D (eds) Additive manufacturing handbook. Taylor & Francis Group, Boca Raton, pp 251–253

    Chapter  Google Scholar 

  7. Gierth M, Henckell P, Ali Y, Scholl J, Bergmann JP (2020) Wire arc additive manufacturing (WAAM) of aluminum alloy AlMg5Mn with energy-reduced gas metal arc welding (GMAW). Materials 13(12):2671

    Article  Google Scholar 

  8. Mukherjee T, DebRoy T (2019) A digital twin for rapid qualification of 3D printed metallic components. Appl Mater Today 14:59–65. https://doi.org/10.1016/j.apmt.2018.11.003

    Article  Google Scholar 

  9. Mostafaei A, Zhao C, He Y, Reza Ghiaasiaan S, Shi B, Shao S, Shamsaei N, Wu Z, Kouraytem N, Sun T, Pauza J, Gordon JV, Webler B, Parab ND, Asherloo M, Guo Q, Chen L, Rollett AD (2022) Defects and anomalies in powder bed fusion metal additive manufacturing. Curr Opin Solid State Mater Sci 26(2):100974. https://doi.org/10.1016/j.cossms.2021.100974

    Article  Google Scholar 

  10. Spears TG, Gold SA (2016) In-process sensing in selective laser melting (SLM) additive manufacturing. Integr Mater Manuf Innov 5(1):16–40. https://doi.org/10.1186/s40192-016-0045-4

    Article  Google Scholar 

  11. Haley JC, Schoenung JM, Lavernia EJ (2018) Observations of particle-melt pool impact events in directed energy deposition. Addit Manuf 22:368–374. https://doi.org/10.1016/j.addma.2018.04.028

    Article  Google Scholar 

  12. Graf B, Marko A, Petrat T, Gumenyuk A, Rethmeier M (2018) 3D laser metal deposition: process steps for additive manufacturing. Weld World 62(4):877–883. https://doi.org/10.1007/s40194-018-0590-x

    Article  Google Scholar 

  13. Lalegani Dezaki M, Serjouei A, Zolfagharian A, Fotouhi M, Moradi M, Ariffin MKA, Bodaghi M (2022) A review on additive/subtractive hybrid manufacturing of directed energy deposition (DED) process. Adv Powder Technol 1(4):100054. https://doi.org/10.1016/j.apmate.2022.100054

    Article  Google Scholar 

  14. Ghanavati R, Naffakh-Moosavy H, Moradi M (2021) Additive manufacturing of thin-walled SS316L-IN718 functionally graded materials by direct laser metal deposition. J Mater Res Technol 15:2673–2685. https://doi.org/10.1016/j.jmrt.2021.09.061

    Article  Google Scholar 

  15. Piscopo G, Iuliano L (2022) Current research and industrial application of laser powder directed energy deposition. Int J Adv Manuf Technol 119(11–12):6893–6917. https://doi.org/10.1007/s00170-021-08596-w

    Article  Google Scholar 

  16. Yan Z, Mei X, Wang W, Pan A, Lin Q, Huang C (2019) Numerical simulation on nanosecond laser ablation of titanium considering plasma shield and evaporation-affected surface thermocapillary convection. Opt Commun 453:124384. https://doi.org/10.1016/j.optcom.2019.124384

    Article  Google Scholar 

  17. Chua ZY, Ahn IH, Moon SK (2017) Process monitoring and inspection systems in metal additive manufacturing: status and applications. Int J Pr Eng Man-GT 4(2):235–245. https://doi.org/10.1007/s40684-017-0029-7

    Article  Google Scholar 

  18. Mani M, Lane BM, Donmez MA, Feng SC, Moylan SP (2017) A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes. Int J Prod Res 55(5):1400–1418. https://doi.org/10.1080/00207543.2016.1223378

    Article  Google Scholar 

  19. de Oliveira AR, Jovičević-Klug M, de Oliveira VF, Teixeira JC, Del Conte EG (2022) Barkhausen noise monitoring of microstructure and surface residual stress in maraging steel manufactured by powder bed fusion and aging. Int J Adv Manuf Technol 119(3):1835–1852. https://doi.org/10.1007/s00170-021-08411-6

    Article  Google Scholar 

  20. Kempen K, Thijs L, Vrancken B, Buls S, Van Humbeeck J, Kruth J (2013) Producing crack-free, high density M2 HSS parts by selective laser melting: pre-heating the baseplate. Proc. 24th Int. Solid Free. Fabr. Symp, University of Texas at Austin

  21. Ranjan R, Yang Y, Ayas C, Langelaar M, Van Keulen F (2017) Controlling local overheating in topology optimization for additive manufacturing. Proceedings of euspen special interest group meeting: additive manufacturing, Leuven, Belgium, KU Leuven, BE

  22. Grasso M, Colosimo BM (2017) Process defects andin situmonitoring methods in metal powder bed fusion: a review. Mater Sci Technol 28(4):044005. https://doi.org/10.1088/1361-6501/aa5c4f

    Article  Google Scholar 

  23. de Oliveira AR, Del Conte EG (2021) Concurrent improvement of surface roughness and residual stress of as-built and aged additively manufactured maraging steel post-processed by milling. Int J Adv Manuf Technol 116(7):2309–2323. https://doi.org/10.1007/s00170-021-07527-z

    Article  Google Scholar 

  24. Moradi M, Hasani A, Pourmand Z, Lawrence J (2021) Direct laser metal deposition additive manufacturing of Inconel 718 superalloy: statistical modelling and optimization by design of experiments. Opt Laser Technol 144:107380. https://doi.org/10.1016/j.optlastec.2021.107380

    Article  Google Scholar 

  25. Tang C, Tan JL, Wong CH (2018) A numerical investigation on the physical mechanisms of single track defects in selective laser melting. Int J Heat Mass Transf 126:957–968. https://doi.org/10.1016/j.ijheatmasstransfer.2018.06.073

    Article  Google Scholar 

  26. Cunningham R, Zhao C, Parab N, Kantzos C, Pauza J, Fezzaa K, Sun T, Rollett AD (2019) Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging. Science 363(6429):849–852. https://doi.org/10.1126/science.aav4687

    Article  Google Scholar 

  27. DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, Beese AM, Wilson-Heid A, De A, Zhang W (2018) Additive manufacturing of metallic components – process, structure and properties. Prog Mater Sci 92:112–224. https://doi.org/10.1016/j.pmatsci.2017.10.001

    Article  Google Scholar 

  28. DebRoy T, Mukherjee T, Wei HL, Elmer JW, Milewski JO (2020) Metallurgy, mechanistic models and machine learning in metal printing. Nat Rev Mater 6(1):48–68. https://doi.org/10.1038/s41578-020-00236-1

    Article  Google Scholar 

  29. Liu M, Kumar A, Bukkapatnam S, Kuttolamadom M (2021) A review of the anomalies in directed energy deposition (DED) processes & potential solutions - part quality & defects. Procedia Manuf 53:507–518. https://doi.org/10.1016/j.promfg.2021.06.093

    Article  Google Scholar 

  30. Malekipour E, El-Mounayri H (2018) Defects, process parameters and signatures for online monitoring and control in powder-based additive manufacturing. Mech Addit Adv Manuf 9:83–90. https://doi.org/10.1007/978-3-319-62834-9_12 (Conference Proceedings of the Society for Experimental Mechanics Series)

    Article  Google Scholar 

  31. Dunbar AJ, Nassar AR (2017) Assessment of optical emission analysis for in-process monitoring of powder bed fusion additive manufacturing. Virtual Phys Prototyp 13(1):14–19. https://doi.org/10.1080/17452759.2017.1392683

    Article  Google Scholar 

  32. Salehi D, Brandt M (2005) Melt pool temperature control using LabVIEW in Nd:YAG laser blown powder cladding process. Int J Adv Manuf Technol 29(3–4):273–278. https://doi.org/10.1007/s00170-005-2514-3

    Article  Google Scholar 

  33. Bi G, Gasser A, Wissenbach K, Drenker A, Poprawe R (2006) Identification and qualification of temperature signal for monitoring and control in laser cladding. Opt Lasers Eng 44(12):1348–1359. https://doi.org/10.1016/j.optlaseng.2006.01.009

    Article  Google Scholar 

  34. Hofman JT, Pathiraj B, van Dijk J, de Lange DF, Meijer J (2012) A camera based feedback control strategy for the laser cladding process. J Mater Process Technol 212(11):2455–2462. https://doi.org/10.1016/j.jmatprotec.2012.06.027

    Article  Google Scholar 

  35. Hofman JT (2009) Development of an observation and control system for industrial laser cladding [Doctoral thesis, University of Twente]. Enschede, Netherland

  36. Song L, Bagavath-Singh V, Dutta B, Mazumder J (2011) Control of melt pool temperature and deposition height during direct metal deposition process. Int J Adv Manuf Technol 58(1–4):247–256. https://doi.org/10.1007/s00170-011-3395-2

    Article  Google Scholar 

  37. Han L, Liou FW, Musti S (2005) Thermal behavior and geometry model of melt pool in laser material process. J Heat Transf 127(9):1005–1014. https://doi.org/10.1115/1.2005275

    Article  Google Scholar 

  38. Lu ZL, Li DC, Lu BH, Zhang AF, Zhu GX, Pi G (2010) The prediction of the building precision in the laser engineered net shaping process using advanced networks. Opt Lasers Eng 48(5):519–525. https://doi.org/10.1016/j.optlaseng.2010.01.002

    Article  Google Scholar 

  39. Farshidianfar MH, Khajepour A, Gerlich A (2015) Real-time control of microstructure in laser additive manufacturing. Int J Adv Manuf Technol 82(5–8):1173–1186. https://doi.org/10.1007/s00170-015-7423-5

    Article  Google Scholar 

  40. Fan Z, Stroble JK, Ruan J, Sparks TE, Liou F (2007) Numerical and analytical modeling of laser deposition with preheating. ASME 2007 International Manufacturing Science and Engineering Conference, Atlanta, Georgia, USA

  41. Tang L a L, RG (2011) Layer-to-layer height control for laser metal deposition process. J Manuf Sci Eng 133(2):021009. https://doi.org/10.1115/1.4003691

  42. Rong-Ji W, Xin-hua L, Qing-ding W, Lingling W (2008) Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm. Int J Adv Manuf Technol 42(11–12):1035–1042. https://doi.org/10.1007/s00170-008-1669-0

    Article  Google Scholar 

  43. Da Sun S, Fabijanic D, Barr C, Liu Q, Walker K, Matthews N, Orchowski N, Easton M, Brandt M (2018) In-situ quench and tempering for microstructure control and enhanced mechanical properties of laser cladded AISI 420 stainless steel powder on 300M steel substrates. Surf Coat Technol 333:210–219. https://doi.org/10.1016/j.surfcoat.2017.10.080

    Article  Google Scholar 

  44. Maamoun AH, Xue YF, Elbestawi MA, Veldhuis SC (2018) Effect of selective laser melting process parameters on the quality of Al alloy parts: powder characterization, density, surface roughness, and dimensional accuracy. Materials (Basel) 11(12). https://doi.org/10.3390/ma11122343

  45. Jiang HZ, Li ZY, Feng T, Wu PY, Chen QS, Fen YL, Li SW, Gao H, Xu HJ (2019) Factor analysis of selective laser melting process parameters with normalised quantities and Taguchi method [Article]. Opt Laser Technol 119(11):Article 105592. https://doi.org/10.1016/j.optlastec.2019.105592

  46. Sun Z, Tan X, Tor SB, Yeong WY (2016) Selective laser melting of stainless steel 316L with low porosity and high build rates. Mater Des 104:197–204. https://doi.org/10.1016/j.matdes.2016.05.035

    Article  Google Scholar 

  47. de Oliveira AR, de Oliveira VF, Teixeira JC, Del Conte EG (2021) Investigation of the build orientation effect on magnetic properties and Barkhausen noise of additively manufactured maraging steel 300. Addit Manuf 38:101827. https://doi.org/10.1016/j.addma.2020.101827

    Article  Google Scholar 

  48. Oliveira AR, Diaz JAA, Nizes ADC, Jardini AL, Del Conte EG (2021) Investigation of building orientation and aging on strength–stiffness performance of additively manufactured maraging steel. J Mater Eng Perform 30(2):1479–1489. https://doi.org/10.1007/s11665-020-05414-4

    Article  Google Scholar 

  49. Dong Z, Liu Y, Wen W, Ge J, Liang J (2018) Effect of hatch spacing on melt pool and as-built quality during selective laser melting of stainless steel: modeling and experimental approaches. Materials (Basel) 12(1):50. https://doi.org/10.3390/ma12010050

    Article  Google Scholar 

  50. Xia M, Gu D, Yu G, Dai D, Chen H, Shi Q (2016) Influence of hatch spacing on heat and mass transfer, thermodynamics and laser processability during additive manufacturing of Inconel 718 alloy. Int J Mach Tools Manuf 109:147–157. https://doi.org/10.1016/j.ijmachtools.2016.07.010

    Article  Google Scholar 

  51. Caiazzo F, Caggiano A (2018) Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning. Materials (Basel) 11(3):444. https://doi.org/10.3390/ma11030444

    Article  Google Scholar 

  52. Shayanfar P, Daneshmanesh H, Janghorban K (2020) Parameters optimization for laser cladding of Inconel 625 on ASTM A592 steel. J Mater Res Technol 9(4):8258–8265. https://doi.org/10.1016/j.jmrt.2020.05.094

    Article  Google Scholar 

  53. Yu T, Sun J, Qu W, Zhao Y, Yang L (2018) Influences of z-axis increment and analyses of defects of AISI 316L stainless steel hollow thin-walled cylinder. Int J Adv Manuf Technol 97(5–8):2203–2220. https://doi.org/10.1007/s00170-018-2083-x

    Article  Google Scholar 

  54. Masaylo D, Igoshin S, Popovich A, Popovich V (2020) Effect of process parameters on defects in large scale components manufactured by direct laser deposition. Mater Today: Proc 30:665–671. https://doi.org/10.1016/j.matpr.2020.01.519

    Article  Google Scholar 

  55. Cao L, Chen S, Wei M, Guo Q, Liang J, Liu C, Wang M (2019) Effect of laser energy density on defects behavior of direct laser depositing 24CrNiMo alloy steel. Opt Laser Technol 111:541–553. https://doi.org/10.1016/j.optlastec.2018.10.025

    Article  Google Scholar 

  56. Shamsaei N, Yadollahi A, Bian L, Thompson SM (2015) An overview of direct laser deposition for additive manufacturing; part II: mechanical behavior, process parameter optimization and control. Addit Manuf 8:12–35. https://doi.org/10.1016/j.addma.2015.07.002

    Article  Google Scholar 

  57. Zenzinger G, Bamberg J, Ladewig A, Hess T, Henkel B, Satzger W (2015) Process monitoring of additive manufacturing by using optical tomography. AIP Conference Proceedings

  58. Joachim Bamberg Gz, Alexander Ladewig (2016) In-process control of selective laser melting by quantitative optical tomography. 19th World Conference on Non-Destructive Testing, Munich, Germany

  59. Clijsters S, Craeghs T, Buls S, Kempen K, Kruth JP (2014) In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system. Int J Adv Manuf Technol 75(5–8):1089–1101. https://doi.org/10.1007/s00170-014-6214-8

    Article  Google Scholar 

  60. Tom Craeghs SC, Evren Yasa, Jean-Pierre Kruth (2011) Online quality control of selective laser melting. Proceedings of the 20th Solid Freeform Fabrication (SFF) symposium, Austin, Texas

  61. Craeghs T, Bechmann F, Berumen S, Kruth J-P (2010) Feedback control of layerwise laser melting using optical sensors. Phys Procedia 5:505–514. https://doi.org/10.1016/j.phpro.2010.08.078

    Article  Google Scholar 

  62. Kruth J-P, Mercelis P, Van Vaerenbergh J, Craeghs T (2007) Feedback control of selective laser melting. Proceedings of the 3rd international conference on advanced research in virtual and rapid prototyping, Leiria, Portugal

  63. Cao X, Ayalew B (2019) Robust multivariable predictive control for laser-aided powder deposition processes. J Franklin Inst 356(5):2505–2529. https://doi.org/10.1016/j.jfranklin.2018.12.015

    Article  MathSciNet  MATH  Google Scholar 

  64. Leung CLA, Marussi S, Atwood RC, Towrie M, Withers PJ, Lee PD (2018) In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing. Nat Commun 9(1):1355. https://doi.org/10.1038/s41467-018-03734-7

    Article  Google Scholar 

  65. Wolff SJ, Wu H, Parab N, Zhao C, Ehmann KF, Sun T, Cao J (2019) In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing. Sci Rep 9(962):1–14. https://doi.org/10.1038/s41598-018-36678-5

    Article  Google Scholar 

  66. Grasso M, Demir AG, Previtali B, Colosimo BM (2018) In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robot Comput-Integr Manuf 49:229–239. https://doi.org/10.1016/j.rcim.2017.07.001

    Article  Google Scholar 

  67. Devesse W, De Baere D, Hinderdael M, Guillaume P (2017) Model-based temperature feedback control of laser cladding using high-resolution hyperspectral imaging. IEEE/ASME Trans Mechatron 22(6):2714–2722. https://doi.org/10.1109/tmech.2017.2754550

    Article  Google Scholar 

  68. Devesse W, De Baere D, Guillaume P (2017) High resolution temperature measurement of liquid stainless steel using hyperspectral imaging. Sensors (Basel) 17(1):91. https://doi.org/10.3390/s17010091

    Article  Google Scholar 

  69. Lison M, Devesse W, de Baere D, Hinderdael M, Guillaume P (2019) Hyperspectral and thermal temperature estimation during laser cladding. J Laser Appl 31(2):022313. https://doi.org/10.2351/1.5096129

    Article  Google Scholar 

  70. Ding Y, Warton J, Kovacevic R (2016) Development of sensing and control system for robotized laser-based direct metal addition system. Addit Manuf 10:24–35. https://doi.org/10.1016/j.addma.2016.01.002

    Article  Google Scholar 

  71. Song L, Wang C, Mazumder J (2012) Identification of phase transformation using optical emission spectroscopy for direct metal deposition process. High power laser materials processing: lasers, beam delivery, diagnostics, and applications, San Francisco, California, United States

  72. Song L, Mazumder J (2011) Real time Cr measurement using optical emission spectroscopy during direct metal deposition process. IEEE Sens J 12(5):958–964. https://doi.org/10.1109/JSEN.2011.2162316

    Article  Google Scholar 

  73. Song L, Huang W, Han X, Mazumder J (2017) Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing. IEEE Trans Industr Electron 64(1):633–642. https://doi.org/10.1109/tie.2016.2608318

    Article  Google Scholar 

  74. Shin J, Mazumder J (2018) Composition monitoring using plasma diagnostics during direct metal deposition (DMD) process. Opt Laser Technol 106:40–46. https://doi.org/10.1016/j.optlastec.2018.03.020

    Article  Google Scholar 

  75. Zhao C, Fezzaa K, Cunningham RW, Wen H, De Carlo F, Chen L, Rollett AD, Sun T (2017) Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction. Sci Rep 7(1):3602. https://doi.org/10.1038/s41598-017-03761-2

    Article  Google Scholar 

  76. Chun C-H (1980) Marangoni convection in a floating zone under reduced gravity. J Cryst Growth 48(4):600–610. https://doi.org/10.1016/0022-0248(80)90271-7

    Article  Google Scholar 

  77. Li Z, Liu X, Wen S, He P, Zhong K, Wei Q, Shi Y, Liu S (2018) In situ 3D monitoring of geometric signatures in the powder-bed-fusion additive manufacturing process via vision sensing methods. Sensors (Basel) 18(4):1180. https://doi.org/10.3390/s18041180

    Article  Google Scholar 

  78. Cheng K, Niu Z-C, Wang RC, Rakowski R, Bateman R (2017) Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin J Mech Eng 30(5):1162–1176. https://doi.org/10.1007/s10033-017-0183-4

    Article  Google Scholar 

  79. Gaja H, Liou F (2016) Defects monitoring of laser metal deposition using acoustic emission sensor. Int J Adv Manuf Technol 90(1–4):561–574. https://doi.org/10.1007/s00170-016-9366-x

    Article  Google Scholar 

  80. Guo WG, Tian Q, Guo S, Guo Y (2020) A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. CIRP Ann 69(1):205–208. https://doi.org/10.1016/j.cirp.2020.04.049

    Article  Google Scholar 

  81. Khanzadeh M, Chowdhury S, Tschopp MA, Doude HR, Marufuzzaman M, Bian L (2018) In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Trans 51(5):437–455. https://doi.org/10.1080/24725854.2017.1417656

    Article  Google Scholar 

  82. Khanzadeh M, Chowdhury S, Marufuzzaman M, Tschopp MA, Bian L (2018) Porosity prediction: supervised-learning of thermal history for direct laser deposition. J Manuf Syst 47:69–82. https://doi.org/10.1016/j.jmsy.2018.04.001

    Article  Google Scholar 

  83. Khanzadeh M, Tian W, Yadollahi A, Doude HR, Tschopp MA, Bian L (2018) Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Addit Manuf 23:443–456. https://doi.org/10.1016/j.addma.2018.08.014

    Article  Google Scholar 

  84. Khanzadeh M, Chowdhury S, Bian L, Tschopp MA (2017) A methodology for predicting porosity from thermal imaging of melt pools in additive manufacturing thin wall sections. International Manufacturing Science and Engineering Conference

  85. Jayasinghe S, Paoletti P, Sutcliffe C, Dardis J, Jones N, Green PL (2021) Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. Prog Addit Manuf 1–18. https://doi.org/10.1007/s40964-021-00219-w

  86. Song L, Mazumder J (2011) Feedback control of melt pool temperature during laser cladding process. IEEE Trans Control Syst Technol 19(6):1349–1356. https://doi.org/10.1109/tcst.2010.2093901

    Article  Google Scholar 

  87. Nassar AR, Keist JS, Reutzel EW, Spurgeon TJ (2015) Intra-layer closed-loop control of build plan during directed energy additive manufacturing of Ti–6Al–4V. Addit Manuf 6:39–52. https://doi.org/10.1016/j.addma.2015.03.005

    Article  Google Scholar 

  88. Nadipalli VK, Andersen SA, Nielsen JS, Pedersen DB (2019) Considerations for interpreting in-situ photodiode sensor data in pulsed mode laser powder bed fusion. Proceedings of the Joint Special Interest Group meeting between euspen and ASPE Advancing Precision in Additive Manufacturing, Nantes, France

  89. Altenburg SJ, Straße A, Gumenyuk A, Maierhofer C (2020) In-situ monitoring of a laser metal deposition (LMD) process: comparison of MWIR, SWIR and high-speed NIR thermography. Quant InfraRed Thermogr J 1–18. https://doi.org/10.1080/17686733.2020.1829889

  90. Ocylok S, Alexeev E, Mann S, Weisheit A, Wissenbach K, Kelbassa I (2014) Correlations of melt pool geometry and process parameters during laser metal deposition by coaxial process monitoring. Phys Procedia 56:228–238. https://doi.org/10.1016/j.phpro.2014.08.167

    Article  Google Scholar 

  91. Kolb T, Elahi R, Seeger J, Soris M, Scheitler C, Hentschel O, Tremel J, Schmidt M (2020) Camera signal dependencies within coaxial melt pool monitoring in laser powder bed fusion. Rapid Prototyp J 26(1):100–106. https://doi.org/10.1108/rpj-01-2019-0022

    Article  Google Scholar 

  92. Donadello S, Motta M, Demir AG, Previtali B (2019) Monitoring of laser metal deposition height by means of coaxial laser triangulation. Opt Lasers Eng 112:136–144. https://doi.org/10.1016/j.optlaseng.2018.09.012

    Article  Google Scholar 

  93. Errico V, Campanelli SL, Angelastro A, Dassisti M, Mazzarisi M, Bonserio C (2021) Coaxial monitoring of AISI 316L thin walls fabricated by direct metal laser deposition. Materials (Basel) 14(3):673. https://doi.org/10.3390/ma14030673

    Article  Google Scholar 

  94. Kolb T, Gebhardt P, Schmidt O, Tremel J, Schmidt M (2018) Melt pool monitoring for laser beam melting of metals: assistance for material qualification for the stainless steel 1.4057. Procedia CIRP 74:116–121. https://doi.org/10.1016/j.procir.2018.08.058

    Article  Google Scholar 

  95. Zhang Y, Hong GS, Ye D, Zhu K, Fuh JYH (2018) Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater Des 156:458–469. https://doi.org/10.1016/j.matdes.2018.07.002

    Article  Google Scholar 

  96. Sampson R, Lancaster R, Sutcliffe M, Carswell D, Hauser C, Barras J (2020) An improved methodology of melt pool monitoring of direct energy deposition processes. Opt Laser Technol 127. https://doi.org/10.1016/j.optlastec.2020.106194

  97. Sampson R, Lancaster R, Sutcliffe M, Carswell D, Hauser C, Barras J (2021) The influence of key process parameters on melt pool geometry in direct energy deposition additive manufacturing systems. Opt Laser Technol 134. https://doi.org/10.1016/j.optlastec.2020.106609

  98. Ye J, Bab-Hadiashar A, Hoseinnezhad R, Alam N, Vargas-Uscategui A, Patel M, Cole I (2022) Predictions of in-situ melt pool geometric signatures via machine learning techniques for laser metal deposition. Int J Comput Integr 1–17. https://doi.org/10.1080/0951192X.2022.2048422

  99. Montazeri M, Nassar AR, Stutzman CB, Rao P (2019) Heterogeneous sensor-based condition monitoring in directed energy deposition. Addit Manuf 30:100916. https://doi.org/10.1016/j.addma.2019.100916

    Article  Google Scholar 

  100. Dunbar AJ, Nassar AR, Reutzel EW, Blecher JJ (2016) A real-time communication architecture for metal powder bed fusion additive manufacturing. Solid Freeform Fabrication Symposium

  101. Montazeri M, Nassar AR, Dunbar AJ, Rao P (2019) In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy. IISE Trans 52(5):500–515. https://doi.org/10.1080/24725854.2019.1659525

    Article  Google Scholar 

  102. Lough CS, Escano LI, Qu M, Smith CC, Landers RG, Bristow DA, Chen L, Kinzel EC (2020) In-situ optical emission spectroscopy of selective laser melting. J Manuf Process 53:336–341. https://doi.org/10.1016/j.jmapro.2020.02.016

    Article  Google Scholar 

  103. Nassar A, Starr B, Reutzel E (2015) Process monitoring of directed-energy deposition of Inconel-718 via plume imaging. Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug

  104. Ye D, Hsi Fuh JY, Zhang Y, Hong GS, Zhu K (2018) In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans 81:96–104. https://doi.org/10.1016/j.isatra.2018.07.021

    Article  Google Scholar 

  105. Repossini G, Laguzza V, Grasso M, Colosimo BM (2017) On the use of spatter signature for in-situ monitoring of laser powder bed fusion. Addit Manuf 16:35–48. https://doi.org/10.1016/j.addma.2017.05.004

    Article  Google Scholar 

  106. Zheng H, Li H, Lang L, Gong S, Ge Y (2018) Effects of scan speed on vapor plume behavior and spatter generation in laser powder bed fusion additive manufacturing. J Manuf Process 36:60–67. https://doi.org/10.1016/j.jmapro.2018.09.011

    Article  Google Scholar 

  107. Yin J, Wang D, Yang L, Wei H, Dong P, Ke L, Wang G, Zhu H, Zeng X (2020) Correlation between forming quality and spatter dynamics in laser powder bed fusion. Addit Manuf 31:100958. https://doi.org/10.1016/j.addma.2019.100958

    Article  Google Scholar 

  108. Yang L, Lo L, Ding S, Özel T (2020) Monitoring and detection of meltpool and spatter regions in laser powder bed fusion of super alloy Inconel 625. Prog Addit Manuf 5(4):367–378. https://doi.org/10.1007/s40964-020-00140-8

    Article  Google Scholar 

  109. Grasso M, Colosimo BM (2019) A statistical learning method for image-based monitoring of the plume signature in laser powder bed fusion. Robot Comput-Integr Manuf 57:103–115. https://doi.org/10.1016/j.rcim.2018.11.007

    Article  Google Scholar 

  110. Taheri H, Koester LW, Bigelow TA, Faierson EJ, Bond LJ (2019) In situ additive manufacturing process monitoring with an acoustic technique: clustering performance evaluation using K-means algorithm. J Manuf Sci Eng 141(4):041011. https://doi.org/10.1115/1.4042786

    Article  Google Scholar 

  111. Koester LW, Taheri H, Bigelow TA, Bond LJ, Faierson EJ (2018) In-situ acoustic signature monitoring in additive manufacturing processes. AIP Conference Proceedings

  112. Eschner N, Weiser L, Häfner B, Lanza G (2020) Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions. Addit Manuf 34:101324. https://doi.org/10.1016/j.addma.2020.101324

    Article  Google Scholar 

  113. Ye DS, Fuh YHJ, Zhang YJ, Hong GS, Zhu KP (2018) Defects recognition in selective laser melting with acoustic signals by SVM based on feature reduction. IOP Conference Series: Materials Science and Engineering

  114. Shi T, Shi J, Xia Z, Lu B, Shi S, Fu G (2020) Precise control of variable-height laser metal deposition using a height memory strategy. J Manuf Process 57:222–232. https://doi.org/10.1016/j.jmapro.2020.05.026

    Article  Google Scholar 

  115. Ye J, Alam N, Vargas-Uscategui A, Patel M, Bab-Hadiashar A, Hoseinnezhad R, Cole I (2022) In situ monitoring of build height during powder-based laser metal deposition. Int J Adv Manuf Technol 122(9):3739–3750. https://doi.org/10.1007/s00170-022-10145-y

    Article  Google Scholar 

  116. Sammons PM, Bristow DA, Landers RG (2019) Two-dimensional modeling and system identification of the laser metal deposition process. J Dyn Syst Meas Control 141(2):021012. https://doi.org/10.1115/1.4041444

    Article  Google Scholar 

  117. Lu QY, Nguyen NV, Hum AJW, Tran T, Wong CH (2019) 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 271:520–531. https://doi.org/10.1016/j.jmatprotec.2019.04.026

    Article  Google Scholar 

  118. Lu QY, Nguyen NV, Hum AJW, Tran T, Wong CH (2020) Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography. Addit Manuf 35:101287. https://doi.org/10.1016/j.addma.2020.101287

    Article  Google Scholar 

  119. Zhang B, Ziegert J, Farahi F, Davies A (2016) In situ surface topography of laser powder bed fusion using fringe projection. Addit Manuf 12:100–107. https://doi.org/10.1016/j.addma.2016.08.001

    Article  Google Scholar 

  120. Grasso M, Laguzza V, Semeraro Q, Colosimo BM (2016) In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. J Manuf Sci Eng 139(5):051001. https://doi.org/10.1115/1.4034715

    Article  Google Scholar 

  121. Pagani L, Grasso M, Scott PJ, Colosimo BM (2020) Automated layerwise detection of geometrical distortions in laser powder bed fusion. Addit Manuf 36:101435. https://doi.org/10.1016/j.addma.2020.101435

    Article  Google Scholar 

  122. Wolff SJ, Webster S, Parab ND, Aronson B, Gould B, Greco A, Sun T (2020) In-situ observations of directed energy deposition additive manufacturing using high-speed X-ray imaging. JOM 73(1):189–200. https://doi.org/10.1007/s11837-020-04469-x

    Article  Google Scholar 

  123. Wolff SJ, Wang H, Gould B, Parab N, Wu Z, Zhao C, Greco A, Sun T (2021) In situ X-ray imaging of pore formation mechanisms and dynamics in laser powder-blown directed energy deposition additive manufacturing. Int J Mach Tools Manuf 166:103743. https://doi.org/10.1016/j.ijmachtools.2021.103743

    Article  Google Scholar 

  124. Webster S, Wolff S, Bennett J, Sun T, Cao J, Ehmann K (2019) Porosity formation and meltpool geometry analysis using high-speed, in situ imaging of directed energy deposition. Microsc Microanal 25(S2):2556–2557. https://doi.org/10.1017/s1431927619013515

    Article  Google Scholar 

  125. Cunningham R, Zhao C, Parab N, Kantzos C, Pauza J, Fezzaa K, Sun T, Rollett AD (2019) Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging. Science 363(6429):849–852. https://doi.org/10.1126/science.aav4687

    Article  Google Scholar 

  126. Bobel A, Hector LG, Chelladurai I, Sachdev AK, Brown T, Poling WA, Kubic R, Gould B, Zhao C, Parab N, Greco A, Sun T (2019) In situ synchrotron X-ray imaging of 4140 steel laser powder bed fusion. Materialia 6. https://doi.org/10.1016/j.mtla.2019.100306

  127. Hojjatzadeh SMH, Parab ND, Yan W, Guo Q, Xiong L, Zhao C, Qu M, Escano LI, Xiao X, Fezzaa K, Everhart W, Sun T, Chen L (2019) Pore elimination mechanisms during 3D printing of metals. Nat Commun 10(1):3088. https://doi.org/10.1038/s41467-019-10973-9

    Article  Google Scholar 

  128. Leung CLA, Marussi S, Towrie M, del Val Garcia J, Atwood RC, Bodey AJ, Jones JR, Withers PJ, Lee PD (2018) Laser-matter interactions in additive manufacturing of stainless steel SS316L and 13–93 bioactive glass revealed by in situ X-ray imaging. Addit Manuf 24:647–657. https://doi.org/10.1016/j.addma.2018.08.025

    Article  Google Scholar 

  129. Zhang B, Jaiswal P, Rai R, Guerrier P, Baggs G (2019) Convolutional neural network-based inspection of metal additive manufacturing parts. Rapid Prototyp J 25(3):530–540. https://doi.org/10.1108/rpj-04-2018-0096

    Article  Google Scholar 

  130. Guo S, Guo WG, Bain L (2020) Hierarchical spatial-temporal modeling and monitoring of melt pool evolution in laser-based additive manufacturing. IISE Trans 1–21. https://doi.org/10.1080/24725854.2019.1704465

  131. Scipioni Bertoli U, Guss G, Wu S, Matthews MJ, Schoenung JM (2017) In-situ characterization of laser-powder interaction and cooling rates through high-speed imaging of powder bed fusion additive manufacturing. Mater Des 135:385–396. https://doi.org/10.1016/j.matdes.2017.09.044

    Article  Google Scholar 

  132. Salehi DS (2005) Sensing and control of Nd:YAG laser cladding process [Doctoral thesis, Swinburne University of Technology]. Melbourne, Australia

  133. Lane B, Heigel J, Ricker R, Zhirnov I, Khromschenko V, Weaver J, Phan T, Stoudt M, Mekhontsev S, Levine L (2020) Measurements of melt pool geometry and cooling rates of individual laser traces on IN625 bare plates. Integr Mater Manuf Innov 9(1):16–30. https://doi.org/10.1007/s40192-020-00169-1

    Article  Google Scholar 

  134. Nadipalli VK, Andersen SA, Nielsen JS, Pedersen DB J n S (2019) Considerations for interpreting in-situ photodiode sensor data in pulsed mode laser powder bed fusion

  135. Scime L, Beuth J (2019) Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process. Addit Manuf 29:100830. https://doi.org/10.1016/j.addma.2019.100830

    Article  Google Scholar 

  136. Xi W, Song B, Zhao Y, Yu T, Wang J (2019) Geometry and dilution rate analysis and prediction of laser cladding. Int J Adv Manuf Technol 103(9–12):4695–4702. https://doi.org/10.1007/s00170-019-03932-7

    Article  Google Scholar 

  137. Feenstra DR, Molotnikov A, Birbilis N (2021) Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications. Mater Des 198:109342. https://doi.org/10.1016/j.matdes.2020.109342

    Article  Google Scholar 

  138. Figueredo EWA, Apolinario LHR, Santos MV, Silva ACS, Avila JA, Lima MSF, Santos TFA (2021) Influence of laser beam power and scanning speed on the macrostructural characteristics of AISI 316L and AISI 431 stainless steel depositions produced by laser cladding process. J Mater Eng Perform 30(5):3298–3312. https://doi.org/10.1007/s11665-021-05676-6

    Article  Google Scholar 

  139. Yadav S, Jinoop AN, Sinha N, Paul CP, Bindra KS (2020) Parametric investigation and characterization of laser directed energy deposited copper-nickel graded layers. Int J Adv Manuf Technol 108(11–12):3779–3791. https://doi.org/10.1007/s00170-020-05644-9

    Article  Google Scholar 

  140. Forien J-B, Calta NP, DePond PJ, Guss GM, Roehling TT, Matthews MJ (2020) Detecting keyhole pore defects and monitoring process signatures during laser powder bed fusion: a correlation between in situ pyrometry and ex situ X-ray radiography. Addit Manuf 35:101336. https://doi.org/10.1016/j.addma.2020.101336

    Article  Google Scholar 

  141. Yuan B, Guss GM, Wilson AC, Hau-Riege SP, DePond PJ, McMains S, Matthews MJ, Giera B (2018) Machine-learning-based monitoring of laser powder bed fusion. Adv Mater Technol 3(12):1800136. https://doi.org/10.1002/admt.201800136

    Article  Google Scholar 

  142. Liu Y, Wang L, Brandt M (2019) Model predictive control of laser metal deposition. Int J Adv Manuf Technol 105(1–4):1055–1067. https://doi.org/10.1007/s00170-019-04279-9

    Article  Google Scholar 

  143. McGowan E, Gawade V, Guo WG (2022) A physics-informed convolutional neural network with custom loss functions for porosity prediction in laser metal deposition. Sensors (Basel) 22(2):494. https://doi.org/10.3390/s22020494

    Article  Google Scholar 

  144. Wei HL, Mukherjee T, Zhang W, Zuback JS, Knapp GL, De A, DebRoy T (2021) Mechanistic models for additive manufacturing of metallic components. Prog Mater Sci 116:100703. https://doi.org/10.1016/j.pmatsci.2020.100703

    Article  Google Scholar 

  145. Du Y, Mukherjee T, DebRoy T (2021) Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects. Appl Mater Today 24:101123. https://doi.org/10.1016/j.apmt.2021.101123

    Article  Google Scholar 

  146. Ghanavati R, Naffakh-Moosavy H, Moradi M, Eshraghi M (2022) Printability and microstructure of directed energy deposited SS316l-IN718 multi-material: numerical modeling and experimental analysis. Sci Rep 12(1):16600. https://doi.org/10.1038/s41598-022-21077-8

    Article  Google Scholar 

  147. Stavropoulos P, Foteinopoulos P (2018) Modelling of additive manufacturing processes: a review and classification. Manuf Rev 5:1–26

    Google Scholar 

  148. Hashemi SM, Parvizi S, Baghbanijavid H, Tan AT, Nematollahi M, Ramazani A, Fang NX, Elahinia M (2022) Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review. Int Mater Rev 67(1):1–46. https://doi.org/10.1080/09506608.2020.1868889

    Article  Google Scholar 

  149. Haley JC, Schoenung JM, Lavernia EJ (2019) Modelling particle impact on the melt pool and wettability effects in laser directed energy deposition additive manufacturing. Mater Sci Eng A 761:138052. https://doi.org/10.1016/j.msea.2019.138052

    Article  Google Scholar 

  150. Song T, Dong T, Lu SL, Kondoh K, Das R, Brandt M, Qian M (2021) Simulation-informed laser metal powder deposition of Ti-6Al-4V with ultrafine α-β lamellar structures for desired tensile properties. Addit Manuf 102139. https://doi.org/10.1016/j.addma.2021.102139

  151. Kledwig C, Perfahl H, Reisacher M, Brückner F, Bliedtner J, Leyens C (2020) Image-based algorithm for nozzle adhesion detection in powder-fed directed-energy deposition. J Laser Appl 32(2):022021. https://doi.org/10.2351/7.0000070

    Article  Google Scholar 

  152. Grasso M, Remani A, Dickins A, Colosimo BM, Leach RK (2021) In-situ measurement and monitoring methods for metal powder bed fusion: an updated review. Mater Sci Technol 32(11):112001. https://doi.org/10.1088/1361-6501/ac0b6b

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support through the provision of the RMIT-CSIRO Scholarship.

Funding

This work was supported by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and its Active Integrated Matter Future Science Platform (AIM FSP) (Grant number [AIM FSP_TB10_WP05]).

Author information

Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia

    Jiayu Ye, Alireza Bab-hadiashar, Nazmul Alam & Ivan Cole

  2. CSIRO Manufacturing, Normanby Road, Clayton, VIC, 3168, Australia

    Jiayu Ye

Authors
  1. Jiayu Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Bab-hadiashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nazmul Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Cole
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design, as well as data collection and analysis. Contribution of knowledge was performed by Nazmul Alam and Ivan Cole. The first draft of the manuscript was written by Jiayu Ye, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jiayu Ye.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, J., Bab-hadiashar, A., Alam, N. et al. A review of the parameter-signature-quality correlations through in situ sensing in laser metal additive manufacturing. Int J Adv Manuf Technol 124, 1401–1427 (2023). https://doi.org/10.1007/s00170-022-10618-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10618-0

Keywords

Search

Navigation