Digitisation of metal AM for part microstructure and property control
Source abstract
Abstract
Abstract Metal additive manufacturing, which uses a layer-by-layer approach to fabricate parts, has many potential advantages over conventional techniques, including the ability to produced complex geometries, fast new design part production, personalised production, have lower cost and produce less material waste. While these advantages make AM an attractive option for industry, determining process parameters which result in specific properties, such as the level of porosity and tensile strength, can be a long and costly endeavour. In this review, the state-of-the-art in the control of part properties in AM is examined, including the effect of microstructure on part properties. The simulation of microstructure formation via numerical simulation and machine learning is examined which can provide process quality control and has the potential to aid in rapid process optimisation via closed loop control. In-situ monitoring of the AM process, is also discussed as a route to enable first time right production in the AM process, along with the hybrid approach of AM fabrication with post-processing steps such as shock peening, heat treatment and rolling. At the end of the paper, an outlook is presented with a view towards potential avenues for further research required in the field of metal AM.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Injection Molding Process and Properties
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References (100+)
Show source-provided references
1. Doğu MN, Esen Z, Davut K et al (2020) Microstructural and texture evolution during thermo-hydrogen processing of Ti6Al4V alloys produced by electron beam melting. Mater Charact 168:110549. https://doi.org/10.1016/j.matchar.2020.11054910.1016/j.matchar.2020.110549 ↗
2. Yusuf SM, Cutler S, Gao N (2019) Review:The Impact of Metal Additive
3. Blakey-Milner B, Gradl P, Snedden G et al (2021) Metal additive manufacturing in aerospace: A review. Mater Des 209:110008. https://doi.org/10.1016/j.matdes.2021.11000810.1016/j.matdes.2021.110008 ↗
4. Barroqueiro B, Andrade-Campos A, Valente RAF, Neto V (2019) Metal Additive Manufacturing Cycle in Aerospace Industry: A Comprehensive Review. J Manuf Mater Process 3:52. https://doi.org/10.3390/jmmp303005210.3390/jmmp3030052 ↗
5. Gisario A, Kazarian M, Martina F, Mehrpouya M (2019) Metal additive manufacturing in the commercial aviation industry: A review. J Manuf Syst 53:124–149. https://doi.org/10.1016/j.jmsy.2019.08.00510.1016/j.jmsy.2019.08.005 ↗
6. Shen J, Zeng Z, Nematollahi M et al (2021) In-situ synchrotron X-ray diffraction analysis of the elastic behaviour of martensite and H-phase in a NiTiHf high temperature shape memory alloy fabricated by laser powder bed fusion. Addit Manuf Lett 1:100003. https://doi.org/10.1016/j.addlet.2021.10000310.1016/j.addlet.2021.100003 ↗
7. Ke WC, Oliveira JP, Cong BQ et al (2022) Multi-layer deposition mechanism in ultra high-frequency pulsed wire arc additive manufacturing (WAAM) of NiTi shape memory alloys. Addit Manuf 50:102513. https://doi.org/10.1016/j.addma.2021.10251310.1016/j.addma.2021.102513 ↗
8. Rodrigues TA, Bairrão N, Farias FWC et al (2022) Steel-copper functionally graded material produced by twin-wire and arc additive manufacturing (T-WAAM). Mater Des 213:110270. https://doi.org/10.1016/j.matdes.2021.11027010.1016/j.matdes.2021.110270 ↗
9. Rodrigues TA, Escobar JD, Shen J et al (2021) Effect of heat treatments on 316 stainless steel parts fabricated by wire and arc additive manufacturing: Microstructure and synchrotron X-ray diffraction analysis. Addit Manuf 48:102428. https://doi.org/10.1016/j.addma.2021.10242810.1016/j.addma.2021.102428 ↗
10. DebRoy T, Wei HL, Zuback JS et al (2018) Additive manufacturing of metallic components – Process, structure and properties. Prog Mater Sci 92:112–22410.1016/j.pmatsci.2017.10.001 ↗
11. Huang W, Yang J, Yang H et al (2019) Heat treatment of Inconel 718 produced by selective laser melting: Microstructure and mechanical properties. Mater Sci Eng A 750:98–107. https://doi.org/10.1016/j.msea.2019.02.04610.1016/j.msea.2019.02.046 ↗
12. Oliveira JP, LaLonde AD, Ma J (2020) Processing parameters in laser powder bed fusion metal additive manufacturing. Mater Des 193:108762. https://doi.org/10.1016/j.matdes.2020.10876210.1016/j.matdes.2020.108762 ↗
13. Jia H, Sun H, Wang H et al (2021) Scanning strategy in selective laser melting (SLM): a review. Int J Adv Manuf Technol 113:2413–2435. https://doi.org/10.1007/s00170-021-06810-310.1007/s00170-021-06810-3 ↗
14. Shi JJ, Li X, Zhang ZX et al (2019) Study on the microstructure and creep behavior of Inconel 718 superalloy fabricated by selective laser melting. Mater Sci Eng A 765:138282. https://doi.org/10.1016/j.msea.2019.13828210.1016/j.msea.2019.138282 ↗
15. Ramakrishnan P (1972) Welding Metallurgy. Indian Weld J 4:89. https://doi.org/10.22486/iwj.v4i3.15024310.22486/iwj.v4i3.150243 ↗
16. Hooper PA (2018) Melt pool temperature and cooling rates in laser powder bed fusion. Addit Manuf 22:548–559. https://doi.org/10.1016/j.addma.2018.05.03210.1016/j.addma.2018.05.032 ↗
17. Song B, Zhao X, Li S et al (2015) Differences in microstructure and properties between selective laser melting and traditional manufacturing for fabrication of metal parts: A review. Front Mech Eng 10:111–125. https://doi.org/10.1007/s11465-015-0341-210.1007/s11465-015-0341-2 ↗
18. Sanchez S, Smith P, Xu Z et al (2021) Powder Bed Fusion of nickel-based superalloys: A review. Int J Mach Tools Manuf 165:103729. https://doi.org/10.1016/j.ijmachtools.2021.10372910.1016/j.ijmachtools.2021.103729 ↗
19. Vrancken B, Thijs L, Kruth J-P, Van Humbeeck J (2014) Microstructure and mechanical properties of a novel β titanium metallic composite by selective laser melting. Acta Mater 68:150–158. https://doi.org/10.1016/j.actamat.2014.01.01810.1016/j.actamat.2014.01.018 ↗
20. Wei HL, Elmer JW, DebRoy T (2016) Origin of grain orientation during solidification of an aluminum alloy. Acta Mater 115:123–131. https://doi.org/10.1016/j.actamat.2016.05.05710.1016/j.actamat.2016.05.057 ↗
21. Li X, Shi JJ, Wang CH et al (2018) Effect of heat treatment on microstructure evolution of Inconel 718 alloy fabricated by selective laser melting. J Alloys Compd 764:639–649. https://doi.org/10.1016/j.jallcom.2018.06.11210.1016/j.jallcom.2018.06.112 ↗
22. Najmon JC, Raeisi S, Tovar A (2019) Review of additive manufacturing technologies and applications in the aerospace industry. Additive Manufacturing for the Aerospace Industry. Elsevier, pp 7–3110.1016/B978-0-12-814062-8.00002-9 ↗
23. Moussaoui K, Rubio W, Mousseigne M et al (2018) Effects of Selective Laser Melting additive manufacturing parameters of Inconel 718 on porosity, microstructure and mechanical properties. Mater Sci Eng A 735:182–190. https://doi.org/10.1016/j.msea.2018.08.03710.1016/j.msea.2018.08.037 ↗
24. Sanchez S, Smith P, Xu Z et al (2021) Powder Bed Fusion of nickel-based superalloys: A review. Int J Mach Tools Manuf 165:103729. https://doi.org/10.1016/j.ijmachtools.2021.10372910.1016/j.ijmachtools.2021.103729 ↗
25. Hosseini E, Popovich VA (2019) A review of mechanical properties of additively manufactured Inconel 718. Addit Manuf 30:100877. https://doi.org/10.1016/j.addma.2019.10087710.1016/j.addma.2019.100877 ↗
26. Yong CK, Gibbons GJ, Wong CC, West G (2020) A Critical Review of the Material Characteristics of Additive Manufactured IN718 for High-Temperature Application. Met (Basel) 10:1576. https://doi.org/10.3390/met1012157610.3390/met10121576 ↗
27. Calandri M, Yin S, Aldwell B et al (2019) Texture and Microstructural Features at Different Length Scales in Inconel 718 Produced by Selective Laser Melting. Mater (Basel) 12:1293. https://doi.org/10.3390/ma1208129310.3390/ma12081293 ↗
28. Gokcekaya O, Ishimoto T, Hibino S et al (2021) Unique crystallographic texture formation in Inconel 718 by laser powder bed fusion and its effect on mechanical anisotropy. Acta Mater 212:116876. https://doi.org/10.1016/j.actamat.2021.11687610.1016/j.actamat.2021.116876 ↗
29. Özer S (2020) Effect of post-processing heat treatment on the mechanical properties of inconel 718 fabricated by selective laser melting. Middle East Technical University
30. Ferreri NC, Vogel SC, Knezevic M (2020) Determining volume fractions of γ, γ′, γ″, δ, and MC-carbide phases in Inconel 718 as a function of its processing history using an advanced neutron diffraction procedure. Mater Sci Eng A 781:139228. https://doi.org/10.1016/j.msea.2020.13922810.1016/j.msea.2020.139228 ↗
31. Tucho WM, Cuvillier P, Sjolyst-Kverneland A, Hansen V (2017) Microstructure and hardness studies of Inconel 718 manufactured by selective laser melting before and after solution heat treatment. Mater Sci Eng A 689:220–232. https://doi.org/10.1016/j.msea.2017.02.06210.1016/j.msea.2017.02.062 ↗
32. Kumara C, Balachandramurthi AR, Goel S et al (2020) Toward a better understanding of phase transformations in additive manufacturing of Alloy 718. Materialia 13:100862. https://doi.org/10.1016/j.mtla.2020.10086210.1016/j.mtla.2020.100862 ↗
33. Suwas S, Ray RK (2014) Deformation Textures. Crystallograhpic Texture of Materials. Springer, pp 95–14110.1007/978-1-4471-6314-5_5 ↗
34. Liu SY, Li HQ, Qin CX et al (2020) The effect of energy density on texture and mechanical anisotropy in selective laser melted Inconel 718. Mater Des 191:108642. https://doi.org/10.1016/j.matdes.2020.10864210.1016/j.matdes.2020.108642 ↗
35. Doğu MN (2019) Production of Ti-6Al-4V alloy by 3D electron beam melting technique and development of itspost treatments. Middle East Technical University
36. Bilgin GM, Esen Z, Akın ŞK, Dericioglu AF (2017) Optimization of the mechanical properties of Ti-6Al-4V alloy fabricated by selective laser melting using thermohydrogen processes. Mater Sci Eng A 700:574–582. https://doi.org/10.1016/j.msea.2017.06.01610.1016/j.msea.2017.06.016 ↗
37. Cao S, Zou Y, Lim CVS, Wu X (2021) Review of laser powder bed fusion (LPBF) fabricated Ti-6Al-4V: process, post-process treatment, microstructure, and property. Light Adv Manuf 2:1. https://doi.org/10.37188/lam.2021.02010.37188/lam.2021.020 ↗
38. Singla AK, Banerjee M, Sharma A et al (2021) Selective laser melting of Ti6Al4V alloy: Process parameters, defects and post-treatments. J Manuf Process 64:161–187. https://doi.org/10.1016/j.jmapro.2021.01.00910.1016/j.jmapro.2021.01.009 ↗
39. Warlimont H (2018) Titanium and titanium alloys. In: Springer Handbooks. Springer, pp 195–20610.1007/978-3-319-69743-7_7 ↗
40. Donachie J, M J (2000) Titanium – A Technical Guide. ASM Int 2nd Ed 55:1023–1026
41. Warlimont H (2018) Titanium and Titanium Alloys. Springer, pp 199–21010.1007/978-3-319-69743-7_7 ↗
42. Cao S, Zou Y, Lim CVS, Wu X (2021) Review of laser powder bed fusion (LPBF) fabricated Ti-6Al-4V: process, post-process treatment, microstructure, and property. Light Adv Manuf 2:1. https://doi.org/10.37188/lam.2021.02010.37188/lam.2021.020 ↗
43. Bilgin GM (2017) Optimization of the mechanical properties of Ti-6Al-4V alloy produced by three dimensional additive manufacturing using termochemical processes, Thesis submitted to the graduate school of Natural and Applied Sciences of Middle East Technical University
44. Zheng Z, Jin X, Bai Y et al (2022) Microstructure and anisotropic mechanical properties of selective laser melted Ti6Al4V alloy under different scanning strategies. Mater Sci Eng A 831:142236. https://doi.org/10.1016/j.msea.2021.14223610.1016/j.msea.2021.142236 ↗
45. Zheng Z, Jin X, Bai Y et al (2022) Microstructure and anisotropic mechanical properties of selective laser melted Ti6Al4V alloy under different scanning strategies. Mater Sci Eng A 831:142236. https://doi.org/10.1016/j.msea.2021.14223610.1016/j.msea.2021.142236 ↗
46. Shipley H, McDonnell D, Culleton M et al (2018) Optimisation of process parameters to address fundamental challenges during selective laser melting of Ti-6Al-4V: A review. Int J Mach Tools Manuf 128:1–20. https://doi.org/10.1016/j.ijmachtools.2018.01.00310.1016/j.ijmachtools.2018.01.003 ↗
47. Simonelli M, Tse YY, Tuck C (2014) On the Texture Formation of Selective Laser Melted Ti-6Al-4V. Metall Mater Trans A 45:2863–2872. https://doi.org/10.1007/s11661-014-2218-010.1007/s11661-014-2218-0 ↗
48. Simonelli M, Tse YY, Tuck C (2014) Effect of the build orientation on the mechanical properties and fracture modes of SLM Ti–6Al–4V. Mater Sci Eng A 616:1–11. https://doi.org/10.1016/j.msea.2014.07.08610.1016/j.msea.2014.07.086 ↗
49. Ueda Y, Fukuda K (1989) New Measuring Method of Three-Dimensional Residual Stresses in Long Welded Joints Using Inherent Strains as Parameters—Lz Method. J Eng Mater Technol 111:1–8. https://doi.org/10.1115/1.322642710.1115/1.3226427 ↗
50. Smith J, Xiong W, Yan W et al (2016) Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support. Comput Mech 57:583–610. https://doi.org/10.1007/s00466-015-1240-410.1007/s00466-015-1240-4 ↗
51. Goldak J, Akhlaghi M (2005) Computational Welding Mechanics. Springer, New York
52. Lindgren LE (2007) Computational Welding Mechanics: Thermomechanical and Microstructural Simulations. Woodhead Publishing10.1201/9781439824092 ↗
53. Tanaka M (2004) An introduction to physical phenomena in arc welding processes. Weld Int 18:845–851. https://doi.org/10.1533/wint.2004.334210.1533/wint.2004.3342 ↗
54. Fotovvati B, Wayne SF, Lewis G, Asadi E (2018) A Review on Melt-Pool Characteristics in Laser Welding of Metals. Adv Mater Sci Eng 2018:1–18. https://doi.org/10.1155/2018/492071810.1155/2018/4920718 ↗
55. Svenungsson J, Choquet I, Kaplan AFH (2015) Laser Welding Process – A Review of Keyhole Welding Modelling. Phys Procedia 78:182–191. https://doi.org/10.1016/j.phpro.2015.11.04210.1016/j.phpro.2015.11.042 ↗
56. Megahed M, Mindt H-W, N’Dri N et al (2016) Metal additive-manufacturing process and residual stress modeling. Integr Mater Manuf Innov 5:61–93. https://doi.org/10.1186/s40192-016-0047-210.1186/s40192-016-0047-2 ↗
57. Schoinochoritis B, Chantzis D, Salonitis K (2017) Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review. Proc Inst Mech Eng Part B J Eng Manuf 231:96–117. https://doi.org/10.1177/095440541456752210.1177/0954405414567522 ↗
58. Khairallah SA, Anderson AT, Rubenchik A, King WE (2016) Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater 108:36–45. https://doi.org/10.1016/j.actamat.2016.02.01410.1016/j.actamat.2016.02.014 ↗
59. Körner C, Attar E, Heinl P (2011) Mesoscopic simulation of selective beam melting processes. J Mater Process Technol 211:978–987. https://doi.org/10.1016/j.jmatprotec.2010.12.01610.1016/j.jmatprotec.2010.12.016 ↗
60. Yan Z, Liu W, Tang Z et al (2018) Review on thermal analysis in laser-based additive manufacturing. Opt Laser Technol 106:427–441. https://doi.org/10.1016/j.optlastec.2018.04.03410.1016/j.optlastec.2018.04.034 ↗
61. Chiumenti M, Neiva E, Salsi E et al (2017) Numerical modelling and experimental validation in Selective Laser Melting. Addit Manuf 18:171–185. https://doi.org/10.1016/j.addma.2017.09.00210.1016/j.addma.2017.09.002 ↗
62. Denlinger ER, Jagdale V, Srinivasan GV et al (2016) Thermal modeling of Inconel 718 processed with powder bed fusion and experimental validation using in situ measurements. Addit Manuf 11:7–15. https://doi.org/10.1016/j.addma.2016.03.00310.1016/j.addma.2016.03.003 ↗
63. Riedlbauer D, Scharowsky T, Singer RF et al (2017) Macroscopic simulation and experimental measurement of melt pool characteristics in selective electron beam melting of Ti-6Al-4V. Int J Adv Manuf Technol 88:1309–1317. https://doi.org/10.1007/s00170-016-8819-610.1007/s00170-016-8819-6 ↗
64. Zhang Z, Huang Y, Rani Kasinathan A et al (2019) 3-Dimensional heat transfer modeling for laser powder-bed fusion additive manufacturing with volumetric heat sources based on varied thermal conductivity and absorptivity. Opt Laser Technol 109:297–312. https://doi.org/10.1016/j.optlastec.2018.08.01210.1016/j.optlastec.2018.08.012 ↗
65. Kollmannsberger S, Carraturo M, Reali A, Auricchio F (2019) Accurate Prediction of Melt Pool Shapes in Laser Powder Bed Fusion by the Non-Linear Temperature Equation Including Phase Changes. Integr Mater Manuf Innov 8:167–177. https://doi.org/10.1007/s40192-019-00132-910.1007/s40192-019-00132-9 ↗
66. Favoretto B, de Hillerin CA, Bettinotti O et al (2019) Reduced order modeling via PGD for highly transient thermal evolutions in additive manufacturing. Comput Methods Appl Mech Eng 349:405–430. https://doi.org/10.1016/j.cma.2019.02.03310.1016/j.cma.2019.02.033 ↗
67. Afrasiabi M, Lüthi C, Bambach M, Wegener K (2021) Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process. Appl Sci 11:2962. https://doi.org/10.3390/app1107296210.3390/app11072962 ↗
68. Ghavamian F, Simone A (2019) Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network. Comput Methods Appl Mech Eng 357:112594. https://doi.org/10.1016/j.cma.2019.11259410.1016/j.cma.2019.112594 ↗
69. Mozaffar M, Paul A, Al-Bahrani R et al (2018) Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manuf Lett 18:35–39. https://doi.org/10.1016/j.mfglet.2018.10.00210.1016/j.mfglet.2018.10.002 ↗
70. Yeung H, Lane B, Fox J (2019) Part geometry and conduction-based laser power control for powder bed fusion additive manufacturing. Addit Manuf 30:100844. https://doi.org/10.1016/j.addma.2019.10084410.1016/j.addma.2019.100844 ↗
71. Bambach M, Fügenschuh A, Buhl J et al (2020) Mathematical Modeling and Optimization for Powder-Based Additive Manufacturing. Procedia Manuf 47:1159–1163. https://doi.org/10.1016/j.promfg.2020.04.15810.1016/j.promfg.2020.04.158 ↗
72. Bähr M, Buhl J, Radow G et al (2021) Stable honeycomb structures and temperature based trajectory optimization for wire-arc additive manufacturing. Optim Eng 22:913–974. https://doi.org/10.1007/s11081-020-09552-510.1007/s11081-020-09552-5 ↗
73. Yao X, Moon SK, Bi G (2017) A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyp J 23:983–997. https://doi.org/10.1108/RPJ-03-2016-004110.1108/RPJ-03-2016-0041 ↗
74. Munguía J, Ciurana J, Riba C (2009) Neural-network-based model for build-time estimation in selective laser sintering. Proc Inst Mech Eng Part B J Eng Manuf 223:995–1003. https://doi.org/10.1243/09544054JEM132410.1243/09544054JEM1324 ↗
75. Di Angelo L, Di Stefano P (2011) A neural network-based build time estimator for layer manufactured objects. Int J Adv Manuf Technol 57:215–224. https://doi.org/10.1007/s00170-011-3284-810.1007/s00170-011-3284-8 ↗
76. Aminzadeh M, Kurfess TR (2019) Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. J Intell Manuf 30:2505–2523. https://doi.org/10.1007/s10845-018-1412-010.1007/s10845-018-1412-0 ↗
77. Scime L, Beuth J (2018) Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf 19:114–126. https://doi.org/10.1016/j.addma.2017.11.00910.1016/j.addma.2017.11.009 ↗
78. Grasso M, Laguzza V, Semeraro Q, Colosimo BM (2017) In-Process Monitoring of Selective Laser Melting: Spatial Detection of Defects Via Image Data Analysis. J Manuf Sci Eng 139. https://doi.org/10.1115/1.403471510.1115/1.4034715 ↗
79. Shevchik SA, Kenel C, Leinenbach C, Wasmer K (2018) Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit Manuf 21:598–604. https://doi.org/10.1016/j.addma.2017.11.01210.1016/j.addma.2017.11.012 ↗
80. Ye D, Hong GS, Zhang Y et al (2018) Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int J Adv Manuf Technol 96:2791–2801. https://doi.org/10.1007/s00170-018-1728-010.1007/s00170-018-1728-0 ↗
81. Wu H, Wang Y, Yu Z (2015) In situ monitoring of FDM machine condition via acoustic emission. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-015-7809-410.1007/s00170-015-7809-4 ↗
82. Wu H, Yu Z, Wang Y (2016) A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission. Volume 3: Joint MSEC-NAMRC Symposia. American Society of Mechanical Engineers10.1115/MSEC2016-8551 ↗
83. Zhang Y, Hong GS, Ye D et al (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.2018.07.00210.1016/j.matdes.2018.07.002 ↗
84. Mahato V, Obeidi MA, Brabazon D, Cunningham P (2020) Detecting voids in 3D printing using melt pool time series data. J Intell Manuf 1–8. https://doi.org/10.1007/S10845-020-01694-8/FIGURES/1110.1007/S10845-020-01694-8/FIGURES/11 ↗
85. McCann R, Obeidi MA, Hughes C et al (2021) In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review. Addit Manuf 45:102058. https://doi.org/10.1016/j.addma.2021.10205810.1016/j.addma.2021.102058 ↗
86. 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.2016.08.00110.1016/j.addma.2016.08.001 ↗
87. Hooper PA (2018) Melt pool temperature and cooling rates in laser powder bed fusion. Addit Manuf 22:548–559. https://doi.org/10.1016/j.addma.2018.05.03210.1016/j.addma.2018.05.032 ↗
88. Ramalho A, Santos TG, Bevans B et al (2022) Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel. Addit Manuf 51:102585. https://doi.org/10.1016/j.addma.2021.10258510.1016/j.addma.2021.102585 ↗
89. Khairallah SA, Anderson AT, Rubenchik A, King WE (2016) Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater 108:36–45. https://doi.org/10.1016/j.actamat.2016.02.01410.1016/j.actamat.2016.02.014 ↗
90. Smurov I, Doubenskaia M (2013) Temperature Monitoring by Optical Methods in Laser Processing. In: Springer Series in Materials Science. pp 375–42210.1007/978-3-642-28359-8_9 ↗
91. Raplee J, Plotkowski A, Kirka MM et al (2017) Thermographic Microstructure Monitoring in Electron Beam Additive Manufacturing. Sci Rep 7:1–16. https://doi.org/10.1038/srep4355410.1038/srep43554 ↗
92. Froend M, Ventzke V, Kashaev N et al (2019) Thermal analysis of wire-based direct energy deposition of Al-Mg using different laser irradiances. Addit Manuf 29:100800. https://doi.org/10.1016/J.ADDMA.2019.10080010.1016/J.ADDMA.2019.100800 ↗
93. Rodriguez E, Mireles J, Terrazas CA et al (2015) Approximation of absolute surface temperature measurements of powder bed fusion additive manufacturing technology using in situ infrared thermography. Addit Manuf 5:31–39. https://doi.org/10.1016/j.addma.2014.12.00110.1016/j.addma.2014.12.001 ↗
94. Criales LE, Arısoy YM, Lane B et al (2017) Laser powder bed fusion of nickel alloy 625: Experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. Int J Mach Tools Manuf 121:22–36. https://doi.org/10.1016/j.ijmachtools.2017.03.00410.1016/j.ijmachtools.2017.03.004 ↗
95. Craeghs T, Clijsters S, Yasa E, Kruth J-P (2011) Online Quality Control of Selective Laser Melting.Proc Solid Free Fabr Symp212–226
96. Neef A, Seyda V, Herzog D et al (2014) Low Coherence Interferometry in Selective Laser Melting. Phys Procedia 56:82–89. https://doi.org/10.1016/j.phpro.2014.08.10010.1016/j.phpro.2014.08.100 ↗
97. Lough CS, Escano LI, Qu M et al (2018) In-Situ Optical Emission Spectroscopy during SLM of 304L Stainless Steel. Solid Free Fabr 2018 Proc 29th Annu Int Solid Free Fabr Symp 2192–2201
98. Rieder H, Dillhöfer A, Spies M et al (2014) Online Monitoring of Additive Manufacturing Processes Using Ultrasound. Proc 11th Eur Conf Non-Destructive Test 1:2194–2201
99. Smith RJ, Hirsch M, Patel R et al (2016) Spatially resolved acoustic spectroscopy for selective laser melting. J Mater Process Technol 236:93–102. https://doi.org/10.1016/j.jmatprotec.2016.05.00510.1016/j.jmatprotec.2016.05.005 ↗
100. Wang Z, Pannier CP, Barton K, Hoelzle DJ (2018) Application of robust monotonically convergent spatial iterative learning control to microscale additive manufacturing. Mechatronics 56:157–165. https://doi.org/10.1016/j.mechatronics.2018.09.00310.1016/j.mechatronics.2018.09.003 ↗