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2000
Volume 18, Issue 1
  • ISSN: 2212-7976
  • E-ISSN: 1874-477X

Abstract

Additive manufacturing overcomes the limitations associated with conventional processes, such as fabricating complex parts, material wastage, and a number of sequential operations. Powder-bed additives fall under the category of additive manufacturing process, which, in recent years, has captured the attention of researchers and scientists working in various fields of science and engineering. Production of powder bed additive manufacturing (PBAM) parts with consistent and predictable properties of powders used during the manufacturing process plays an important role in deciding printed parts' reliability in aeronautical, automobile, biomedical, and healthcare applications. In the PBAM process, the most commonly used powders are polymer, metal, and ceramic, which cannot be effectively used without understanding powder particles' physical, mechanical, and chemical properties. Several metallic powders like titanium, steel, copper, aluminum, and nickel, several polymer polyamides (nylon), polylactide, polycarbonate, glass-filled nylon, epoxy resins, ., and the most commonly used ceramic powders like aluminum oxide (AlO) and zirconium oxide (ZrO) can be utilized depending upon the method being adopted during PBAM process. Adoption of some post-processing techniques for powder, such as grain refinement can also be employed to improve the physical or mechanical properties of powders used for the PBAM process. In this paper, the effect of powder parameters, such as particle size, shape, density, and reusing of powder, ., on printed parts have been reviewed in detail using characterization techniques such as X-ray computed tomography, scanning electron microscopy, and X-ray photoelectron spectroscopy. This helps to understand the effect of particle size, shape, density, virgin and reused powders, ., used during the PBAM process. This article has reviewed the selection of appropriate process parameters like laser power, scanning speed, hatch spacing, and layer thickness and their effects on various mechanical or physical properties, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. One of the drawbacks of additive manufacturing is the variability in the quality of printed parts, which can be eliminated by monitoring the process using machine learning techniques. Also, the prediction of the best combination of process parameters using some advanced machine learning algorithms (MLA), like random forest, k nearest neighbors, and support vector machine, can be effectively utilized to quantify the performance parameters in the PBAM process. Thus, implementing machine learning in the additive manufacturing process not only helps to learn the fundamentals but helps to identify, predict and help to make actionable recommendations that help optimize printed parts quality. The performance of various MLAs has been evaluated and compared for projecting future research directions and suggestions. In the last part of this article, multidisciplinary applications of the PBAM process have been reviewed in detail. Additive manufacturing processes carried out by using conventional machines, called hybrid additive manufacturing, have also been reviewed by discussing their methods and arrangements in detail. Lastly this review contributes to the understanding of the PBAM process and is a valuable resource for potential patent applications related to additive manufacturing areas.

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References

  1. ShanmugamR RamoniMO ChandranJ MohanavelV PugazhendhiL A review on the significant classification of additive manufacturing.J Phys: Conf Ser20212027012026.s10.1088/1742‑6596/2027/1/012026
     [Google Scholar]
  2. NgoT.D. KashaniA. ImbalzanoG. NguyenK.T.Q. HuiD. Additive manufacturing (3D printing): A review of materials, methods, applications and challenges.Compos., Part B Eng.201814317219610.1016/j.compositesb.2018.02.012
     [Google Scholar]
  3. WuestT. WeimerD. IrgensC. ThobenK.D. Machine learning in manufacturing: Advantages, challenges, and applications.Prod. Manuf. Res.201641234510.1080/21693277.2016.1192517
     [Google Scholar]
  4. HastieT. TibshiraniR. FriedmanJ.H. FriedmanJ.H. The elements of statistical learning: Data mining, inference, and prediction.New YorkSpringer200910.1007/978‑0‑387‑84858‑7
     [Google Scholar]
  5. BeamanJ.J. DeckardC.R. Selective laser sintering with assisted powder handling.US Patent 4938816A1990
  6. LadaniL. SadeghilaridjaniM. Review of powder bed fusion additive manufacturing for metals.Metals2021119139110.3390/met11091391
     [Google Scholar]
  7. LadaniL. Additive Manufacturing of Metals Materials, Processes, Tests, and Standards.DEStech Publications, Inc.2021
     [Google Scholar]
  8. FidanI. HuseynovO. AliM.A. Recent inventions in additive manufacturing: Holistic review.Inventions20238410310.3390/inventions8040103
     [Google Scholar]
  9. ParikhP. SharmaA. TrivediR. RoyD. JoshiK. Performance evaluation of an indigenously-designed high performance dynamic feeding robotic structure using advanced additive manufacturing technology, machine learning and robot kinematics.Int J Inter Des Manu20231990993710.1007/s12008‑023‑01513‑3
     [Google Scholar]
  10. SinghA.K. KumarS. SinghV.P. Optimization of parameters using conductive powder in dielectric for EDM of super Co 605 with multiple quality characteristics.Mater. Manuf. Process.201429326727310.1080/10426914.2013.864397
     [Google Scholar]
  11. SinghA.K. KumarS. SinghV.P. Effect of the addition of conductive powder in dielectric on the surface properties of superalloy Super Co 605 by EDM process.Int. J. Adv. Manuf. Technol.2015771-49910610.1007/s00170‑014‑6433‑z
     [Google Scholar]
  12. KaliaG. SharmaA. BabbarA. Use of three-dimensional printing techniques for developing biodegradable applications: A review investigation.Mater. Today Proc.202262134635210.1016/j.matpr.2022.03.445
     [Google Scholar]
  13. HaeriS. HaeriS. HansonJ. LotfianS. Analysis of radiation pressure and aerodynamic forces acting on powder grains in powder-based additive manufacturing.Powder Technol.202036812512910.1016/j.powtec.2020.04.031
     [Google Scholar]
  14. AmbhoreN. KambleD. ChinchanikarS. Evaluation of cutting tool vibration and surface roughness in hard turning of AISI 52100 steel: An experimental and ANN approach.J Vib Eng Technol20208345546210.1007/s42417‑019‑00136‑x
     [Google Scholar]
  15. AmbhoreN. KambleD. ChinchanikarS. WayalV. Tool condition monitoring system: A review.Mater. Today Proc.201524-53419342810.1016/j.matpr.2015.07.317
     [Google Scholar]
  16. LadaniL.J. Applications of artificial intelligence and machine learning in metal additive manufacturing.J Phys Mater2021404200910.1016/j.matpr.2022.03.445
     [Google Scholar]
  17. BadugeS.K. ThilakarathnaS. PereraJ.S. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications.Autom. Construct.202214110444010.1016/j.autcon.2022.104440
     [Google Scholar]
  18. JohnsonN.S. VulimiriP.S. ToA.C. Machine learning for materials developments in metals additive manufacturing.arXiv:2005052352020
     [Google Scholar]
  19. HeinrichK. ZschechP. JanieschC. BoninM. Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning.Decis. Support Syst.202114311349410.1016/j.dss.2021.113494
     [Google Scholar]
  20. LemuH.G. On opportunities and limitations of additive manufacturing technology for industry 4.0 era.In: Advanced Manufacturing and Automation VIII 8.Springer Singapore201910611310.1007/978‑981‑13‑2375‑1_15
     [Google Scholar]
  21. KumarS. GopiT. HarikeerthanaN. Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control.J. Intell. Manuf.2023341215510.1007/s10845‑022‑02029‑5
     [Google Scholar]
  22. ChandramowliswaranA KumarJ PrasadK Application of machine learning based classification algorithms in additive manufacturing-A review.Advancements In Aeromechanical Materials For Manufacturing: ICAAMM-2021. 27–28 August 2021; Hyderabad, India202310.1063/5.0113275
     [Google Scholar]
  23. GuanJ. Intelligent bioprinting for structure and mechanical property modulation. San Diego (CA).University of California San Diego2023https://escholarship.org/uc/item/24w189xk
     [Google Scholar]
  24. SanaeiN. FatemiA. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review.Prog. Mater. Sci.202111710072410.1016/j.pmatsci.2020.100724
     [Google Scholar]
  25. WangC. TanX.P. TorS.B. LimC.S. Machine learning in additive manufacturing: State-of-the-art and perspectives.Addit. Manuf.20203610153810.1016/j.addma.2020.101538
     [Google Scholar]
  26. LiuR. KumarA. ChenZ. AgrawalA. SundararaghavanV. ChoudharyA. A predictive machine learning approach for microstructure optimization and materials design.Sci. Rep.2015511155110.1038/srep11551 26100717
     [Google Scholar]
  27. LiS. HassaninH. AttallahM.M. AdkinsN.J.E. EssaK. The development of TiNi-based negative Poisson’s ratio structure using selective laser melting.Acta Mater.2016105758310.1016/j.actamat.2015.12.017
     [Google Scholar]
  28. ZhangY. YangS. DongG. ZhaoY.F. Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model.Addit. Manuf.20214110194610.1016/j.addma.2021.101946
     [Google Scholar]
  29. WestphalE. SeitzH. A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks.Addit. Manuf.20214110196510.1016/j.addma.2021.101965
     [Google Scholar]
  30. AminiM. ChangS.I. MLCPM: A process monitoring framework for 3D metal printing in industrial scale.Comput. Ind. Eng.201812432233010.1016/j.cie.2018.07.041
     [Google Scholar]
  31. ObatonA.F. WangY. ButschB. HuangQ. A non-destructive resonant acoustic testing and defect classification of additively manufactured lattice structures.Weld. World202165336137110.1007/s40194‑020‑01034‑7
     [Google Scholar]
  32. KorneevS. WangZ. ThiagarajanV. NelaturiS. Fabricated shape estimation for additive manufacturing processes with uncertainty.Comput. Aided Des.202012710285210.1016/j.cad.2020.102852
     [Google Scholar]
  33. AdnanM. LuY. JonesA. ChengF.T. YeungH. A new architectural approach to monitoring and controlling AM processes.Appl. Sci.20201018661610.3390/app10186616
     [Google Scholar]
  34. MycroftW. KatzmanM. WilliamsT.S. A data-driven approach for predicting printability in metal additive manufacturing processes.J. Intell. Manuf.20203171769178110.1007/s10845‑020‑01541‑w
     [Google Scholar]
  35. CoatanéaE. NagarajanH.P.N. PanickerS.B. Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: A case study for additive manufacturing.Int. J. Adv. Manuf. Technol.2021115715731
     [Google Scholar]
  36. MahmoudiM. TapiaG. FrancoB. On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing.J. Manuf. Process.20183567268010.1016/j.jmapro.2018.08.037
     [Google Scholar]
  37. WasmerK. Le-QuangT. MeylanB. ShevchikS.A. In situ quality monitoring in AM using acoustic emission: A reinforcement learning approach.J. Mater. Eng. Perform.201928266667210.1007/s11665‑018‑3690‑2
     [Google Scholar]
  38. ShevchikS.A. MasinelliG. KenelC. LeinenbachC. WasmerK. Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission.IEEE Trans. Industr. Inform.20191595194520310.1109/TII.2019.2910524
     [Google Scholar]
  39. ScimeL. BeuthJ. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm.Addit. Manuf.20181911412610.1016/j.addma.2017.11.009
     [Google Scholar]
  40. ShevchikS.A. KenelC. LeinenbachC. WasmerK. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks.Addit. Manuf.20182159860410.1016/j.addma.2017.11.012
     [Google Scholar]
  41. MorenoG.A.I. OrozcoA.J.M. MedinaI.J. FrancoM.E. Ex-situ porosity classification in metallic components by laser metal deposition: A machine learning-based approach.J. Manuf. Process.20216252353410.1016/j.jmapro.2020.12.048
     [Google Scholar]
  42. SnowZ. DiehlB. ReutzelE.W. NassarA. Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning.J. Manuf. Syst.202159122610.1016/j.jmsy.2021.01.008
     [Google Scholar]
  43. LiuR. LiuS. ZhangX. A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing.Int. J. Adv. Manuf. Technol.20211137-81943195810.1007/s00170‑021‑06640‑3
     [Google Scholar]
  44. YadavP. SinghV.K. JoffreT. Inline drift detection using monitoring systems and machine learning in selective laser melting.Adv. Eng. Mater.20202212200066010.1002/adem.202000660
     [Google Scholar]
  45. ZhangX. SaniieJ. ClearyW. HeifetzA. Quality control of additively manufactured metallic structures with machine learning of thermography images.J. Miner. Met. Mater. Soc.202072124682469410.1007/s11837‑020‑04408‑w
     [Google Scholar]
  46. MitchellJ.A. IvanoffT.A. DagelD. MadisonJ.D. JaredB. Linking pyrometry to porosity in additively manufactured metals.Addit. Manuf.20203110094610.1016/j.addma.2019.100946
     [Google Scholar]
  47. XiangyangY. QiY. JunjunL. LinZ. Atomic simulations of melting behaviours for TiAl alloy nanoparticles during heating.Bull. Mater. Sci.202043124110.1007/s12034‑020‑02193‑5
     [Google Scholar]
  48. YazdiM.R. ImaniF. YangH. A hybrid deep learning model of process-build interactions in additive manufacturing.J. Manuf. Syst.20205746046810.1016/j.jmsy.2020.11.001
     [Google Scholar]
  49. ScimeL. BeuthJ. Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process.Addit. Manuf.20192515116510.1016/j.addma.2018.11.010
     [Google Scholar]
  50. ZhangY. SafdarM. XieJ. LiJ. SageM. ZhaoY.F. A systematic review on data of additive manufacturing for machine learning applications: The data quality, type, preprocessing, and management.J. Intell. Manuf.20223433053340
     [Google Scholar]
  51. GuG.X. ChenC.T. RichmondD.J. BuehlerM.J. Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment.Mater. Horiz.20185593994510.1039/C8MH00653A
     [Google Scholar]
  52. RoachD.J. RohskopfA. HamelC.M. Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures.Addit. Manuf.20214110195010.1016/j.addma.2021.101950
     [Google Scholar]
  53. SrinivasanS. SwickB. GroeberM.A. Laser powder bed fusion parameter selection via machine-learning-augmented process modeling.J. Miner. Met. Mater. Soc.202072124393440310.1007/s11837‑020‑04383‑2
     [Google Scholar]
  54. ParkH.S. Machine learning-based optimization of process parameters in selective laser melting for biomedical applications.J. Intell. Manuf.20213318431858
     [Google Scholar]
  55. BaturynskaI. Application of machine learning techniques to predict the mechanical properties of polyamide 2200 (PA12) in additive manufacturing.Appl. Sci.201996106010.3390/app9061060
     [Google Scholar]
  56. ZhanZ. LiH. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing.Int. J. Fatigue202114510608910.1016/j.ijfatigue.2020.106089
     [Google Scholar]
  57. ChangT.W. LiaoK.W. LinC.C. TsaiM.C. ChengC.W. Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning.Int. J. Adv. Manuf. Technol.20211149-103177318410.1007/s00170‑021‑07037‑y
     [Google Scholar]
  58. LiuS. StebnerA.P. KappesB.B. ZhangX. Machine learning for knowledge transfer across multiple metals additive manufacturing printers.Addit. Manuf.20213910187710.1016/j.addma.2021.101877
     [Google Scholar]
  59. HerriottC. SpearA.D. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods.Comput. Mater. Sci.202017510959910.1016/j.commatsci.2020.109599
     [Google Scholar]
  60. ZhangM. SunC.N. ZhangX. High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach.Int. J. Fatigue201912810519410.1016/j.ijfatigue.2019.105194
     [Google Scholar]
  61. OkaroI.A. JayasingheS. SutcliffeC. BlackK. PaolettiP. GreenP.L. Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning.Addit. Manuf.201927425310.1016/j.addma.2019.01.006
     [Google Scholar]
  62. GaikwadA. GieraB. GussG.M. ForienJ.B. MatthewsM.J. RaoP. Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion – A single-track study.Addit. Manuf.20203610165910.1016/j.addma.2020.101659
     [Google Scholar]
  63. LiuQ. WuH. PaulM.J. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms.Acta Mater.202020131632810.1016/j.actamat.2020.10.010
     [Google Scholar]
  64. HassaninH. AlkendiY. ElsayedM. EssaK. ZweiriY. Controlling the properties of additively manufactured cellular structures using machine learning approaches.Adv. Eng. Mater.2020223190133810.1002/adem.201901338
     [Google Scholar]
  65. MarmarelisM.G. GhanemR.G. Data-driven stochastic optimization on manifolds for additive manufacturing.Comput. Mater. Sci.202018110975010.1016/j.commatsci.2020.109750
     [Google Scholar]
  66. ZhanZ. LiH. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L.Int. J. Fatigue202114210594110.1016/j.ijfatigue.2020.105941
     [Google Scholar]
  67. RankouhiB. JahaniS. PfefferkornF.E. ThomaD.J. Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters.Addit. Manuf.20213810183610.1016/j.addma.2021.101836
     [Google Scholar]
  68. HuangD.J. LiH. A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing.Mater. Des.202120310960610.1016/j.matdes.2021.109606
     [Google Scholar]
  69. ZouhriW. DantanJ.Y. HäfnerB. Optical process monitoring for Laser-Powder Bed Fusion (L-PBF).CIRO J. Manuf. Sci. Technol.20203160761710.1016/j.cirpj.2020.09.001
     [Google Scholar]
  70. HertleinN. DeshpandeS. VenugopalV. KumarM. AnandS. Prediction of selective laser melting part quality using hybrid Bayesian network.Addit. Manuf.20203210108910.1016/j.addma.2020.101089
     [Google Scholar]
  71. GarlandA.P. WhiteB.C. JaredB.H. HeidenM. DonahueE. BoyceB.L. Deep convolutional neural networks as a rapid screening tool for complex additively manufactured structures.Addit. Manuf.20203510121710.1016/j.addma.2020.101217
     [Google Scholar]
  72. SuttonA.T. KriewallC.S. LeuM.C. NewkirkJ.W. Powders for additive manufacturing processes: Characterization techniques and effects on part properties. Solid Freeform Fabrication 2016: Procee dings of the 27th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference.10041030
     [Google Scholar]
  73. RielliV.V. PiglioneA. PhamM.S. PrimigS. On the detailed morphological and chemical evolution of phases during laser powder bed fusion and common post-processing heat treatments of IN718.Addit. Manuf.20225010254010.1016/j.addma.2021.102540
     [Google Scholar]
  74. AhsanF. RazmiJ. LadaniL. Process parameter optimization in metal laser-based powder bed fusion using image processing and statistical analyses.Metals20221218710.3390/met12010087
     [Google Scholar]
  75. SinglaA.K. BanerjeeM. SharmaA. Selective laser melting of Ti6Al4V alloy: Process parameters, defects and post-treatments.J. Manuf. Process.20216416118710.1016/j.jmapro.2021.01.009
     [Google Scholar]
  76. BiriaieS.S. NouariM. BoubakerH.B. LaheurteP. Effect of additive manufacturing process parameters on the titanium alloy microstructure, properties and surface integrity.Procedia CIRP202210881181610.1016/j.procir.2022.03.126
     [Google Scholar]
  77. StarrT.L. RafiK. StuckerB. ScherzerC.M. Controlling phase composition in selective laser melted stainless steels.2012 International Solid Freeform Fabrication Symposium4416
     [Google Scholar]
  78. MurrL.E. MartinezE. HernandezJ. Microstructures and properties of 17-4 PH stainless steel fabricated by selective laser melting.J. Mater. Res. Technol.20121316717710.1016/S2238‑7854(12)70029‑7
     [Google Scholar]
  79. KingW.E. AndersonA.T. FerenczR.M. Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges.Appl. Phys. Rev.20152404130410.1063/1.4937809
     [Google Scholar]
  80. ZhongY. RännarL.E. LiuL. Additive manufacturing of 316L stainless steel by electron beam melting for nuclear fusion applications.J. Nucl. Mater.201748623424510.1016/j.jnucmat.2016.12.042
     [Google Scholar]
  81. CalignanoF. TommasiT. ManfrediD. ChiolerioA. Additive manufacturing of a microbial fuel cell—a detailed study.Sci. Rep.2015511737310.1038/srep17373 26611142
     [Google Scholar]
  82. UddinS.Z. MurrL.E. TerrazasC.A. MortonP. RobersonD.A. WickerR.B. Processing and characterization of crack-free aluminum 6061 using high-temperature heating in laser powder bed fusion additive manufacturing.Addit. Manuf.20182240541510.1016/j.addma.2018.05.047
     [Google Scholar]
  83. LouX. SongM. EmighP.W. OthonM.A. AndresenP.L. On the stress corrosion crack growth behaviour in high temperature water of 316L stainless steel made by laser powder bed fusion additive manufacturing.Corros. Sci.201712814015310.1016/j.corsci.2017.09.017
     [Google Scholar]
  84. IrrinkiH. HarperT. BadweS. Effects of powder characteristics and processing conditions on the corrosion performance of 17-4 PH stainless steel fabricated by laser-powder bed fusion.Prog Addit Manuf201831-2394910.1007/s40964‑018‑0048‑0
     [Google Scholar]
  85. KerschbaumerM. ErnstG. Hybrid manufacturing process for rapid high performance tooling combining high speed milling and laser cladding.In: Proceedings of the 23rd International Congress on Applications of Lasers & Electro-Optics2004 Oct 4–7San Francisco, CAOrlando (FL)Laser Institute of America200417102010.2351/1.5060234
     [Google Scholar]
  86. ArntzK. WegenerM. LiuY. Computer aided manufacturing supported process planning of additive manufacturing by laser deposition welding.J. Laser Appl.201527S1S1400210.2351/1.4823748
     [Google Scholar]
  87. SongM. WangM. LouX. RebakR.B. WasG.S. Radiation damage and irradiation-assisted stress corrosion cracking of additively manufactured 316L stainless steels.J. Nucl. Mater.2019513334410.1016/j.jnucmat.2018.10.044
     [Google Scholar]
  88. SommerD. GötzendorferB. EsenC. HellmannR. Design rules for hybrid additive manufacturing combining selective laser melting and micromilling.Materials20211419575310.3390/ma14195753 34640151
     [Google Scholar]
  89. RonnebergT. DaviesC.M. HooperP.A. Revealing relationships between porosity, microstructure and mechanical properties of laser powder bed fusion 316L stainless steel through heat treatment.Mater. Des.202018910848110.1016/j.matdes.2020.108481
     [Google Scholar]
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Powder Bed Additive Manufacturing Using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook
Recent Patents on Mechanical Engineering 18, 12 (2025); https://doi.org/10.2174/0122127976289578240319102303
/content/journals/meng/10.2174/0122127976289578240319102303
/content/journals/meng/10.2174/0122127976289578240319102303
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  • Article Type:     Review Article
Keyword(s): characterization techniques; hybrid additive manufacturing; Machine learning; mechanical properties; powder bed additive manufacturing; shape memory alloys

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