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Publishing Language: Chinese

Research progress on laser selective melting technology for high-performance manufacturing of aero-engines

Yunlong ZHOU1, Yi MA1, Yingchun GUAN1,2( )
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
National Engineering Laboratory of Additive Manufacturing for Large Metallic Components, Beihang University, Beijing 100191, China
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Abstract

Additive manufacturing technology has the capability of breaking through traditional manufacturing constraints, thus enabling the integrated design and manufacturing of complex geometric structures. Simultaneously, this technology contributes to enhancing the reliability of product components with extensive potential applications in the aerospace industry. To address the common issues related to the process and performance, this study, focusing on the Selective Laser Melting (SLM) technique within additive manufacturing for aircraft engines, initially revisits the regulation of material structure and properties through a perspective of technical optimization, including large-area multi-beam technology and field-assisted techniques. Subsequently, by delving into cutting-edge techniques such as quality online monitoring and intelligent machine learning control, the optimization role of monitoring and predicting in the early and middle stages of the forming process is discussed. Then, aiming at providing better guidance for material selection and performance control, the key aerospace engine materials for SLM formation is systematically summarized. Lastly, a review of current SLM technology solutions and types of aerospace engine materials is concluded, along with an outlook on future development prospects so as to provide valuable insights for the field of aerospace engine manufacturing.

Keywords

aero-engineselective laser meltingenergy field assistancemachine learningonline monitoringaerospace materials
CLC number: V261.8; V263 Document code: A

References

1
TAN C L, WENG F, SUI S, et al. Progress and perspectives in laser additive manufacturing of key aeroengine materials [J]. International Journal of Machine Tools and Manufacture, 2021, 170: 103804.
Crossref Google Scholar
2
GUO N N, LEU M C. Additive manufacturing: technology, applications and research needs [J]. Frontiers of Mechanical Engineering, 2013, 8 (3): 215-243.
Crossref Google Scholar
3
XU Z Y, WANG Y D. Electrochemical machining of complex components of aero-engines: developments, trends, and technological advances [J]. Chinese Journal of Aeronautics, 2021, 34 (2): 28-53.
Crossref Google Scholar
4
CHEN C Y, WANG J, WANG R X, et al. Research progress and prospect of additive manufacturing of key materials for aeroengines and gas turbines [J]. Science and Technology Review, 2023, 41 (5): 34-48 (in Chinese).
Google Scholar
5
ZHANG H Z, BAI J, MA R, et al. Current progress and application of selective laser melting technology in aerospace engine manufacturing [J]. Journal of Propulsion Technology, 2023, 44 (3): 6-21 (in Chinese).
Google Scholar
6
THE ANTABEAR 3D PRINGTING TECHNOLOGY. General additive business liu zhigang: GE aviation will 3D print nearly 40, 000 metal parts in 2019 [EB/OL]. (2018-07-29) [2023-08-25]. http://www.nanjixiong.com/thread-129232-1-1.html (in Chinese).
7
AN G J. Application and prospect of metal additive manufacturing technology in aerospace [J]. Modern Machinery, 2019 (3): 39-43 (in Chinese).
Google Scholar
8
ZHAO Z G, BO L, LI L, et al. Status and progress of selective laser melting forming technology [J]. Aeronautical Manufacturing Technology, 2014 (19): 46-49 (in Chinese).
Google Scholar
9
SHIRLEY-3D. Metal 3D printing technology - SLM application cases [EB/OL]. (2020-08-09) [2023-08-25]. https://www.sohu.com/a/412277436_120652671 (in Chinese).
10
ZHANG M X, LIU C M, SHI X Z, et al. Residual stress, defects and grain morphology of Ti-6Al-4V alloy produced by ultrasonic impact treatment assisted selective laser melting [J]. Applied Sciences, 2016, 6 (11): 304.
Crossref Google Scholar
11
JI Z, HAN Q. A novel image feature descriptor for SLM spattering pattern classification using a consumable camera [J]. The International Journal of Advanced Manufacturing Technology, 2020, 110: 2955-2976.
Crossref Google Scholar
12
ZOU M, JIANG W G, QIN Q H, et al. Optimized XGBoost model with small dataset for predicting relative density of Ti-6Al-4V parts manufactured by selective laser melting [J]. Materials, 2022, 15 (15): 5298.
Crossref Google Scholar
13
ZHOU A, LIU X B, LIU Q S, et al. Progress of in-process monitoring techniques for selective laser melting [J]. China Surface Engineering, 2023, 36 (4): 36-50 (in Chinese).
Google Scholar
14
NI J T, ZHOU Q J, YI F, et al. Development of laser additive manufacturing technology and its application progress in aerospace field [J]. Chinese Journal of Rare Metals, 2022, 46 (10): 1365-1382 (in Chinese).
Google Scholar
15
GU D D, MEINERS W, WISSENBACH K, et al. Laser additive manufacturing of metallic components: materials, processes and mechanisms [J]. International Materials Reviews, 2012, 57 (3): 133-164.
Crossref Google Scholar
16
ABOULKHAIR T N, MASKERY I, TUCK C, et al. Improving the fatigue behaviour of a selectively laser melted aluminium alloy: influence of heat treatment and surface quality [J]. Materials and Design, 2016, 104: 174-182.
Crossref Google Scholar
17
ZENG Q P, FU G, REN Z H, et al. Progress and prospect of multi-beam selective laser melting [J/OL]. Journal of Materials Engineering, (2023-02-14) [2023-08-20]. http://kns.cnki.net/kcms/detail/11.1800.TB.20230214.1111.002.html (in Chinese).
Google Scholar
18
LEI Y, FANG L J, SUN B B, et al. Microstructures and mechanical properties of GH4169 alloy fabricated by multi-laser beam selective laser melting [J]. Welding Technology, 2020, 49 (7): 27-32, 5-6 (in Chinese).
Google Scholar
19
SANCHEZ S, HYDE C J, ASHCROFT I A, et al. Multi-laser scan strategies for enhancing creep performance in LPBF [J]. Additive Manufacturing, 2021, 41: 101948.
Crossref Google Scholar
20
LIU J, DONG S Y, JIN X, et al. Quality control of large-sized alloy steel parts fabricated by multi-laser selective laser melting (ML-SLM) [J]. Materials and Design, 2022, 223: 111209.
Crossref Google Scholar
21
WEI K W, LI F Z, HUANG G, et al. Multi-laser powder bed fusion of Ti-6Al-4V alloy: defect, microstructure, and mechanical property of overlap region [J]. Materials Science and Engineering A, 2021, 802: 140644.
Crossref Google Scholar
22
LI F Z, WANG Z M, ZENG X Y. Microstructures and mechanical properties of Ti6Al4V alloy fabricated by multi-laser beam selective laser melting [J]. Materials Letters, 2017, 199: 79-83.
Crossref Google Scholar
23
YIN J, WANG D Z, WEI H L, et al. Dual-beam laser-matter interaction at overlap region during multi-laser powder bed fusion manufacturing [J]. Additive Manufacturing, 2021, 46: 102178.
Crossref Google Scholar
24
YANG S K, XIE Y K, HU Q D, et al. Effect of scan order of overlap area on forming qualities of TC4 alloy fabricated by selective dual-beam laser melting technique [J]. Aeronautical Manufacturing Technology, 2022, 65 (5): 93-99 (in Chinese).
Google Scholar
25
LI P, SHEN H B, WANG Z M, et al. Effect of splicing strategy on microstructure and properties of TA15 titanium alloy formed by multi-beam laser selective melting [J]. Defense Manufacturing Technology, 2021 (4): 27-30 (in Chinese).
Google Scholar
26
ZHANG S Y, WANG M, WANG C, et al. The effect of overlap methods on forming qualities of TC4 alloy fabricated by multi-beam selective laser melting technique [J]. Applied Laser, 2019, 39 (4): 544-549 (in Chinese).
Google Scholar
27
LIU B, KUAI Z Z, LI Z H, et al. Performance consistency of AlSi10Mg alloy manufactured by simulating multi laser beam selective laser melting (SLM): microstructures and mechanical properties [J]. Materials, 2018, 11 (12): 2354.
Crossref Google Scholar
28
LI Z H, LIU W P, LIU B, et al. Difference-extent of microstructure and mechanical properties: simulating multi-laser selective melting Ti6Al4V [J]. Optics and Laser Technology, 2022, 153: 108249.
Crossref Google Scholar
29
SHE B Z. Fundamental study on multi-beam selective laser melting of TA15 alloy [D]. Wuhan: Huazhong University of Science and Technology, 2019: 87-88 (in Chinese).
30
CEN W H, TANG H L, ZHANG J Z, et al. Scanning strategy to improve the overlapping quality of partition in selective laser melting [J]. Chinese Journal of Lasers, 2021, 48 (18): 1802018 (in Chinese).
Crossref Google Scholar
31
WANG D, WANG H, LIU Z X, et al. Influence mechanism of laser delay on internal defect and surface quality in stitching region of 316L stainless steel fabricated by dual-laser selective laser melting [J]. Journal of Manufacturing Processes, 2023, 94: 35-48.
Crossref Google Scholar
32
HEELING T, WEGENER K. The effect of multi-beam strategies on selective laser melting of stainless steel 316L [J]. Additive Manufacturing, 2018, 22: 334-342.
Crossref Google Scholar
33
ATTARIANI H, PETITJEAN S R, DOUSTI M. A digital twin of synchronized circular laser array for powder bed fusion additive manufacturing [J]. The International Journal of Advanced Manufacturing Technology, 2022, 123: 1433-1440.
Crossref Google Scholar
34
HE C, RAMANI K S, OKWUDIRE C E. An intelligent scanning strategy (SmartScan) for improved part quality in multi-laser PBF additive manufacturing [J]. Additive Manufacturing, 2023, 64: 103427.
Crossref Google Scholar
35
BERGMUELLER S, SCHEIBER J, KASERER L, et al. Enhancing equiaxed grain formation in a high-alloy tool steel using dual laser powder bed fusion [J]. Additive Manufacturing, 2023, 74: 103727.
Crossref Google Scholar
36
AMANO H, ISHIMOTO T, HAGIHARA K, et al. Impact of gas flow direction on the crystallographic texture evolution in laser beam powder bed fusion [J]. Virtual and Physical Prototyping, 2023, 18 (1): e2169172.
Crossref Google Scholar
37
BAEHR S, KLECKER T, PIELMEIER S, et al. Experimental and analytical investigations of the removal of spatters by various process gases during the powder bed fusion of metals using a laser beam [J/OL]. Progress in Additive Manufacturing, (2023-08-05) [2023-10-13]. https://doi.org/10.1007/s40964-023-00491-y.
Crossref Google Scholar
38
KJER M B, PAN Z, NADIMPALLI V K, et al. Experimental analysis and spatial component impact of the inert cross flow in open-architecture laser powder bed fusion [J]. Journal of Manufacturing and Materials Processing, 2023, 7 (4): 143.
Crossref Google Scholar
39
ZOU S, XIAO H B, YE F P, et al. Numerical analysis of the effect of the scan strategy on the residual stress in the multi-laser selective laser melting [J]. Results in Physics, 2020, 16: 103005.
Crossref Google Scholar
40
XU G G, JIANG W G, SUN Y Y, et al. Particle-scale computational fluid dynamics simulation on selective parallel dual-laser melting of nickel-based superalloy [J]. Journal of Manufacturing Processes, 2022, 73: 197-206.
Crossref Google Scholar
41
PROMOPPATUM P. Dual-laser powder bed fusion additive manufacturing: computational study of the effect of process strategies on thermal and residual stress formations [J]. The International Journal of Advanced Manufacturing Technology, 2022, 121: 1337-1351.
Crossref Google Scholar
42
LI H L, FU G, REN Z H, et al. Numerical simulation of molten pool dynamics in multi-beam laser selective fusion splicing region [J]. Surface Technology, 2023, 52 (11): 406-418 (in Chinese).
Google Scholar
43
BALL A K, BASAK A. AI Modeling for high-fidelity heat transfer and thermal distortion forecast in metal additive manufacturing [J]. The International Journal of Advanced Manufacturing Technology, 2023, 128: 2995-3010.
Crossref Google Scholar
44
GU D D, SHI X Y, POPRAWE R, et al. Material-structure-performance integrated laser-metal additive manufacturing [J]. Science, 2021, 372 (6545): eabg1487.
Crossref Google Scholar
45
WEI C, LI L. Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion [J]. Virtual and Physical Prototyping, 2021, 16 (3): 1-25.
Crossref Google Scholar
46
XIE Y, TENG Q, SHEN M Y, et al. Study on microstructure and properties of overlap region of GH3536 alloy processed by multi-laser powder bed fusion [J]. Chinese Journal of Lasers, 2023, 50 (8): 0802303 (in Chinese).
Crossref Google Scholar
47
LI Z H, KUAI Z Z, BAI P K, et al. Microstructure and tensile properties of AlSi10Mg alloy manufactured by multi-laser beam selective laser melting (SLM) [J]. Metals, 2019, 9 (12): 1337.
Crossref Google Scholar
48
WEN S F, YAN C Z, WEI Q S, et al. Investigation and development of large-scale equipment and high performance materials for powder bed laser fusion additive manufacturing [J]. Virtual and Physical Prototyping, 2014, 9 (4): 213-223.
Crossref Google Scholar
49
LIU Z W, HOU C J, WANG L F, et al. Study on selective multi-laser beam melting technology [J]. Manufacturing Technology and Machine Tool, 2018 (1): 56-59 (in Chinese).
Google Scholar
50
SOLUTIONS SLM. Meet the NXG XII 600: A new era in manufacturing [EB/OL]. [2023-08-27]. https://www.slm-pushing-the-limits.com/specs#material-properties.
51
BRIGHT LASER TECHNOLOGIES. BLT-S600: Bigger than bigger, let’s achieve more [EB/OL]. [2023-08-27]. https://www.xa-blt.com/en/product/blt-s600/.
52
WANG Z M, HUANG W P, ZENG X Y. Status and prospect of selective laser melting machines [J]. Journal of Netshape Forming Engineering, 2019, 11 (4): 21-28 (in Chinese).
Google Scholar
53
FAN S C, YIN F S, REN Z Q, et al. Research status of multi-energy field composite additive manufacturing technology based on arc [J]. Surface Technology, 2023, 52 (8): 49-70 (in Chinese).
Google Scholar
54
HU Y B. Recent progress in field-assisted additive manufacturing: materials, methodologies, and applications [J]. Materials Horizons, 2021, 8 (3): 885-911.
Crossref Google Scholar
55
SAFAEE S, SCHOCK M, JOYEE E B, et al. Field-assisted additive manufacturing of polymeric composites [J]. Additive Manufacturing, 2022, 51: 102642.
Crossref Google Scholar
56
ZHAO Z Y, BAI P K, LIU B, et al. A selective laser melting SLM forming cylinder under strong magnetic field: China, CN205888085U [P]. 2017-01-18 (in Chinese).
57
LIU S, LI H, SHEN S N, et al. The invention relates to a thermal-magnetic coupling field cooperative selective laser melting device and a heating method thereof: China, CN108421976A [P]. 2019-09-17 (in Chinese).
58
FAN W, TAN H, LIN X, et al. Microstructure formation of Ti-6Al-4V in synchronous induction assisted laser deposition [J]. Materials and Design, 2018, 160: 1096-1105.
Crossref Google Scholar
59
TODARO C J, EASTON M A, QIU D, et al. Grain structure control during metal 3D printing by high-intensity ultrasound [J]. Nature Communications, 2020, 11 (1): 142.
Crossref Google Scholar
60
FAN X Q, FLEMING T G, REES D T, et al. Thermoelectric magnetohydrodynamic control of melt pool flow during laser directed energy deposition additive manufacturing [J]. Additive Manufacturing, 2023, 71: 103587.
Crossref Google Scholar
61
KANG N, YUAN H, CODDET P, et al. On the texture, phase and tensile properties of commercially pure Ti produced via selective laser melting assisted by static magnetic field [J]. Materials Science and Engineering C, 2017, 70: 405-407.
Crossref Google Scholar
62
DU D F, HALEY C J, DONG A P, et al. Influence of static magnetic field on microstructure and mechanical behavior of selective laser melted AlSi10Mg alloy [J]. Materials and Design, 2019, 181: 107923.
Crossref Google Scholar
63
KANG N, CODDET P, WANG J, et al. A novel approach to in-situ produce functionally graded silicon matrix composite materials by selective laser melting [J]. Composite Structures, 2017, 172: 251-258.
Crossref Google Scholar
64
CHENG T, ZHANG Z Y, LIU Y B, et al. Effects of online static magnetic field on anisotropy of microstructure and mechanical properties of GH3536 fabricated by selective laser melting [J]. Chinese Journal of Lasers, 2022, 49 (8): 0802017 (in Chinese).
Crossref Google Scholar
65
ZHANG B, SHIRVANI K, TAHERI M, et al. Effect of TiC and magnetic field on microstructure and mechanical properties of IN738 superalloy processed by selective laser melting [J]. Journal of Materials Engineering and Performance, 2024, 33: 3494-3509.
Crossref Google Scholar
66
XI L X, LU Q Y, GU D D. A method for grain refinement of Al-Cu alloy modified by ultrasonic processing assisted laser 3D printing nanoparticles: China, CN113600833A [P]. 2022-10-11 (in Chinese).
67
TAHERI M, RAZAVI M. The effect of ultrasonic field on the microstructure and corrosion behavior of Fe-based amorphous coating applied to selective laser melting [J]. Materials Research Express, 2023, 10 (7): 076508.
Crossref Google Scholar
68
MÜLLER A V, SCHLICK G, NEU R, et al. Additive manufacturing of pure tungsten by means of selective laser beam melting with substrate preheating temperatures up to 1000 ℃ [J]. Nuclear Materials and Energy, 2019, 19: 184-188.
Crossref Google Scholar
69
WANG J, FAUTRELLE Y, REN Z M, et al. Thermoelectric magnetic force acting on the solid during directional solidification under a static magnetic field [J]. Applied Physics Letters, 2012, 101 (25): 251904.
Crossref Google Scholar
70
HU S D, HOU L, WANG K, et al. Effect of transverse static magnetic field on radial microstructure of hypereutectic aluminum alloy during directional solidification [J]. Journal of Materials Science and Technology, 2021, 76 (17): 207-214.
Crossref Google Scholar
71
LI X, FAUTRELLE Y, GAGNOUD A, et al. Effect of a weak transverse magnetic field on solidification structure during directional solidification [J]. Acta Materialia, 2014, 64: 367-381.
Crossref Google Scholar
72
ZHOU H X, SONG C H, YANG Y Q, et al. The microstructure and properties evolution of SS316L fabricated by magnetic field-assisted laser powder bed fusion [J]. Materials Science and Engineering A, 2022, 845: 143216.
Crossref Google Scholar
73
DU D F, DONG A P, SUN B D, et al. The invention relates to a method for reducing the porosity of a laser selective melting forming member: China, CN112974803A [P]. 2022-08-23 (in Chinese).
74
CHEN L W, LI H, LIU S, et al. Simulation of surface deformation control during selective laser melting of AlSi10Mg powder using an external magnetic field [J]. AIP Advances, 2019, 9 (4): 045012.
Crossref Google Scholar
75
ZHU W L, YU S, CHEN C Y, et al. Effects of static magnetic field on the microstructure of selective laser melted inconel 625 superalloy: numerical and experiment investigations [J]. Metals, 2021, 11 (11): 1846.
Crossref Google Scholar
76
ZHOU Z, YAO C F, ZHAO Y, et al. Effect of ultrasonic impact treatment on the surface integrity of nickel alloy 718 [J]. Advances in Manufacturing, 2021, 9: 160-171.
Crossref Google Scholar
77
CAO Y, ZHANG Y C, MING W Y, et al. Review: the metal additive-manufacturing technology of the ultrasonic-assisted wire-and-arc additive-manufacturing process [J]. Metals, 2023, 13 (2): 398.
Crossref Google Scholar
78
YUAN D, SHAO S Q, GUO C H, et al. Grain refining of Ti-6Al-4V alloy fabricated by laser and wire additive manufacturing assisted with ultrasonic vibration [J]. Ultrasonics Sonochemistry, 2021, 73: 105472.
Crossref Google Scholar
79
TAN C L, LI R S, SU J L, et al. Review on field assisted metal additive manufacturing [J]. International Journal of Machine Tools and Manufacture, 2023, 189: 104032.
Crossref Google Scholar
80
CONG W L, NING F D. A fundamental investigation on ultrasonic vibration-assisted laser engineered net shaping of stainless steel [J]. International Journal of Machine Tools and Manufacture, 2017, 121: 61-69.
Crossref Google Scholar
81
ŻRODOWSKI Ł, CHOMA T, WILKOS I, et al. Influence of surface characteristics and finishing on fatigue properties of additively manufactured Ti6A14V [C] //2021 6th International Conference on Nanotechnology for Instrumentation and Measurement. 2021: 1-4.
Crossref
82
GE Y Q, XU H J, CHANG Z X, et al. A multi-energy field synergistic gradient material preparation system: China, CN116021034A [P]. 2023-04-28 (in Chinese).
83
KIM B S, LEE N, THOTA S, et al. Effects of radiative local heating on metal solidification during selective laser melting for additive manufacturing [J]. Applied Surface Science, 2019, 496: 143594.
Crossref Google Scholar
84
FAN W, TAN H, ZHANG F Y, et al. Overcoming the limitation of in-situ microstructural control in laser additive manufactured Ti-6Al-4V alloy to enhanced mechanical performance by integration of synchronous induction heating [J]. Journal of Materials Science and Technology, 2021, 94: 32-46.
Crossref Google Scholar
85
HUANG X N, CAO Y B, YUE W, et al. The control of internal defects during laser melting deposition by external physical field [J]. Powder Metallurgy Industry, 2023, 33 (1): 107-114 (in Chinese).
Google Scholar
86
SHIM D S, BAEK GY, LEE E M. Effect of substrate preheating by induction heater on direct energy deposition of AISI M4 powder [J]. Materials Science and Engineering A, 2017, 682: 550-562.
Crossref Google Scholar
87
DALAEE M T, GLOOR L, LEINENBACH C, et al. Experimental and numerical study of the influence of induction heating process on build rates induction heating-assisted laser direct metal deposition (IH-DMD) [J]. Surface and Coatings Technology, 2020, 384: 125275.
Crossref Google Scholar
88
LIAO W H, LIU T T, ZHANG K, et al. A selective laser melting forming equipment for ceramic materials: China, CN104016686A [P]. 2015-11-11 (in Chinese).
89
PARKER N, HEFFORD S, LEES J, et al. A novel VHF heating system to aid selective laser melting [C] //2019 IEEE MTT-S International Microwave Symposium. 2019: 975-978.
Crossref
90
ZHANG P, ZHU Q, WANG M, et al. A laser selective melting discharge composite process and equipment: China, CN114260463A [P]. 2022-05-27 (in Chinese).
91
WU B, JI X Y, ZHOU J X, et al. In situ monitoring methods for selective laser melting additive manufacturing process based on images-a review [J]. China Foundry, 2021, 18 (4): 265-285.
Crossref Google Scholar
92
CAO L C, ZHOU Q, HAN Y F, et al. Review on intelligent monitoring of defects and process control of selective laser melting additive manufacturing [J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (10): 524790 (in Chinese).
Google Scholar
93
EVERTON S K, HIRSCH M, STRAVROULAKIS P, et al. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing [J]. Materials and Design, 2016, 95: 431-445.
Crossref Google Scholar
94
GRASSO M, COLOSIMO B M. Process defects and in situ monitoring methods in metal powder bed fusion: a review [J]. Measurement Science and Technology, 2017, 28 (4): 044005.
Crossref Google Scholar
95
LIN Z Q, LAI Y W, PAN T T, et al. A new method for automatic detection of defects in selective laser melting based on machine vision [J]. Materials, 2021, 14: 4175.
Crossref Google Scholar
96
YANG D K, LI H, LIU S, et al. In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm [J]. Optics and Laser Technology, 2020, 131: 106371.
Crossref Google Scholar
97
MAZZOLENI L, DEMIR A G, CAPRIO L, et al. Real-time observation of melt pool in selective laser melting: Spatial, temporal, and wavelength resolution criteria [J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69 (4): 1179-1190.
Crossref Google Scholar
98
LUO S, MA X, XU J, et al. Deep learning based monitoring of spatter behavior by the acoustic signal in selective laser melting [J]. Sensors, 2021, 21: 7179.
Crossref Google Scholar
99
LI J C, ZHOU Q, HUANG X F, et al. In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting [J]. Journal of Intelligent Manufacturing, 2023, 34: 853-867.
Crossref Google Scholar
100
LI J C, CAO L C, XU J, et al. In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing [J]. Measurement, 2022, 187: 110232.
Crossref Google Scholar
101
WANG D, WANG Y M, YANG Y Q, et al. The invention relates to a coaxial monitoring method and device for laser selective melting process: China, CN106984813A [P]. 2019-08-20 (in Chinese).
102
STOLIDI A, TOURON A, TOULEMONDE L, et al. Towards in-situ fumes composition monitoring during an additive manufacturing process using energy dispersive X-ray fluorescence spectrometry [J]. Additive Manufacturing Letters, 2023, 6: 100153.
Crossref Google Scholar
103
FANG Q H, TAN Z B, LI H, et al. In-situ capture of melt pool signature in selective laser melting using U-Net-Based convolutional neural network [J]. Journal of Manufacturing Processes, 2021, 68: 347-355.
Crossref Google Scholar
104
XING W, CHU X, LYU T Y, et al. Using convolutional neural networks to classify melt pools in a pulsed selective laser melting process [J]. Journal of Manufacturing Processes, 2022, 74: 486-499.
Crossref Google Scholar
105
LE T N, LO Y L, LIN Z H. Numerical simulation and experimental validation of melting and solidification process in selective laser melting of IN718 alloy [J]. Additive Manufacturing, 2020, 36: 101519.
Crossref Google Scholar
106
PAVLOV M, DOUBENSKAIA M, SMUROV I. Pyrometric analysis of thermal processes in SLM technology [J]. Physics Procedia, 2010, 5: 523-531.
Crossref Google Scholar
107
ZHENG L P, ZHANG Q, CAO H Z, et al. Melt pool boundary extraction and its width prediction from infrared images in selective laser melting [J]. Materials and Design, 2019, 183: 108110.
Crossref Google Scholar
108
BRUNA ROSSO C, CAPRIO L, MAZZOLENI L, et al. Influence of temporal laser emission profile on the selective laser melting (SLM) of thin structures [J]. Lasers in Engineering, 2020, 47: 161-182.
Google Scholar
109
HUSSAIN S Z, KAUSAR Z, KORESHI Z U, et al. Feedback control of melt pool area in selective laser melting additive manufacturing process [J]. Processes, 2021, 9: 1547.
Crossref Google Scholar
110
RENKEN V, FREYBERG A, SCHÜNEMANN K, et al. In-process closed-loop control for stabilising the melt pool temperature in selective laser melting [J]. Progress in Additive Manufacturing, 2019, 4 (4): 411-421.
Crossref Google Scholar
111
OSTER S, MAIERHOFER C, MOHR G, et al. Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermography [C] //Proceedings of SPIE 11743, Thermosense: Thermal Infrared Applications XLIII. 2021: 117430C.
Crossref
112
WANG X, LOUGH C S, BRISTOW D A, et al. A layer-to-layer control-oriented model for selective laser melting [C] //2020 American Control Conference (ACC). 2020: 481-486.
Crossref
113
SHEVCHIK S A, KENEL C, LENIENBACH C, et al. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks [J]. Additive Manufacturing, 2018, 21: 598-604.
Crossref Google Scholar
114
XI L X, GU D D, HOU J X, et al. A real-time control laser additive alloy manufacturing method based on online ultrasonic monitoring is presented: China, CN116652208A [P]. 2023-08-29 (in Chinese).
115
ITO K, KUSANO M, DEMURA M, et al. Detection and location of microdefects during selective laser melting by wireless acoustic emission measurement [J]. Additive Manufacturing, 2021, 40: 101915.
Crossref Google Scholar
116
PLOTNIKOV Y, HENKEL D, BURDICK J, et al. Infrared-assisted acoustic emission process monitoring for additive manufacturing [J]. AIP Conference Proceedings, 2019, 2102 (1): 020006.
Crossref Google Scholar
117
VAN Belle L, VANSTEENKISTE G, BOYER J C. Investigation of residual stresses induced during the selective laser melting process [J]. Key Engineering Materials, 2013, 554-557: 1828-1834.
Crossref Google Scholar
118
LI Z R, LIANG W D, FAN Z J, et al. Advances in online detection technology for laser additive manufacturing: a review [J]. 3D Printing and Additive Manufacturing, 2023, 10 (3): 467-489.
Crossref Google Scholar
119
BOSCHETTO A, BOTTINI L, VATANPARAST S. Powder bed monitoring via digital image analysis in additive manufacturing [J]. Journal of Intelligent Manufacturing, 2024, 35: 991-1011.
Crossref Google Scholar
120
BOSCHETTO A, BOTTINI L, VATANPARAST S, et al. Part defects identification in selective laser melting via digital image processing of powder bed anomalies [J]. Production Engineering, 2022, 16: 691-704.
Crossref Google Scholar
121
MCCANN R, OBEIDI M A, HUGHES C, et al. In-situ sensing, process monitoring and machine control in laser powder bed fusion: a review [J]. Additive Manufacturing, 2021, 45, 102058.
Crossref Google Scholar
122
LU Q Y, NGUYEN N V, HUM A J W, et al. Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography [J]. Additive Manufacturing, 2020, 35: 101287.
Crossref Google Scholar
123
MODARESIALAM M, ROOZBAHANI H, ALIZADEH M, et al. In-situ monitoring and defect detection of selective laser melting process and impact of process parameters on the quality of fabricated SS 316L [J]. IEEE Access, 2022, 10: 46100-46113.
Crossref Google Scholar
124
YADAV P, RIGO O, ARVIEU C, et al. Data treatment of in situ monitoring systems in selective laser melting machines [J]. Advanced Engineering Materials, 2021, 23: 2001327.
Crossref Google Scholar
125
LIN X, LIU B, SHEN A C, et al. Collaborative control for in situ monitoring of molten pool in selective laser melting [J]. Frontiers in Mechanical Engineering, 2023, 9: 1123751.
Crossref Google Scholar
126
LU Y Q, WONG H C. Additive manufacturing process monitoring and control by non-destructive testing techniques: challenges and in-process monitoring [J]. Virtual and Physical Prototyping, 2018, 13 (2): 39-48.
Crossref Google Scholar
127
COECK S, BISHT M, PLAS J, et al. Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data [J]. Additive Manufacturing, 2019, 25: 347-356.
Crossref Google Scholar
128
PANDIYAN V, DRISSI-DAOUDI R, SHEVCHIK S, et al. Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions [J]. Virtual and Physical Prototyping, 2021, 16 (4): 481-497.
Crossref Google Scholar
129
ESCHNER N, WEISER L, HÄFNER B, et al. Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions [J]. Additive Manufacturing, 2020, 34: 101324.
Crossref Google Scholar
130
WANG H, LI B, XUAN F Z. Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition [J]. The International Journal of Advanced Manufacturing Technology, 2022, 122: 2277-2292.
Crossref Google Scholar
131
LI J C, ZHOU Q, CAO L C, et al. A convolutional neural network-based multi-sensor fusion approach for in-situ quality monitoring of selective laser melting [J]. Journal of Manufacturing Systems, 2022, 64: 429-442.
Crossref Google Scholar
132
LI J C, ZHANG X G, ZHOU Q, et al. A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting [J]. Journal of Manufacturing Processes, 2022, 84, 913-926.
Crossref Google Scholar
133
LIN X, ZHU K P, FUH J Y H, et al. Metal-based additive manufacturing condition monitoring methods: from measurement to control [J]. ISA Transactions, 2022, 120: 147-166.
Crossref Google Scholar
134
SAMPEDRO G A R, RACHMAWATI S M, KIM D S, et al. Exploring machine learning-based fault monitoring for polymer-based additive manufacturing: challenges and opportunities [J]. Sensors, 2022, 22 (23): 9446.
Crossref Google Scholar
135
KLESZCZYNSKI S, JACOBSMÜHLEN J ZUR, REINARZ B, et al. Improving process stability of laser beam melting systems [C] //Fraunhofer Direct Digital Manufacturing Conference, 2014.
136
LI Y X, ZHAO W, LI Q S, et al. In-situ monitoring and diagnosing for fused filament fabrication process based on vibration sensors [J]. Sensors, 2019, 19 (11): 2589.
Crossref Google Scholar
137
SONG J F, SONG Y N, WANG W W, et al. Prediction and control on the surface roughness of metal powder using selective laser melting [J]. Chinese Journal of Lasers, 2022, 49 (2): 0202008 (in Chinese).
Crossref Google Scholar
138
ZHANG B C, DEMBINSKI L, CODDET C. The study of the laser parameters and environment variables effect on mechanical properties of high compact parts elaborated by selective laser melting 316L powder [J]. Materials Science and Engineering A, 2013, 584: 21-31.
Crossref Google Scholar
139
SU J L, CHEN L Q, TAN C L, et al. Progress in machine-learning-assisted process optimization and novel material development in additive manufacturing [J]. Chinese Journal of Lasers, 2022, 49 (14): 1402101 (in Chinese).
Crossref Google Scholar
140
ROHANINEJAD M, TAVAKKOLI M R, VAHEDI N B, et al. A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines [J]. International Journal of Production Research, 2022, 60 (20): 6205-6225.
Crossref Google Scholar
141
CHEN Y Y, WANG H Z, WU Y, et al. Predicting the printability in selective laser melting with a supervised machine learning method [J]. Materials, 2020, 13 (22): 5063.
Crossref Google Scholar
142
ZHANG S F, LI B, XUAN F Z. Signal denoising and classification prediction method for on-line monitoring of acoustic emission during laser melting process [J/OL]. Journal of Mechanical Engineering, (2022-04-21) [2023-08-22]. http://kns.cnki.net/kcms/detail/11.2187.TH.20220526.1827.102.html (in Chinese).
Google Scholar
143
CHANG T W, LIAO K W, LIN C C, et al. Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning [J]. The International Journal of Advanced Manufacturing Technology, 2021, 114: 3177-3184.
Crossref Google Scholar
144
LIAN Y P, GAN Z T, YU C, et al. A cellular automaton finite volume method for microstructure evolution during additive manufacturing [J]. Materials and Design, 2019, 169: 107672.
Crossref Google Scholar
145
CHEN H Y, LIN C C, HORNG M H, et al. Deep learning applied to defect detection in powder spreading process of magnetic material additive manufacturing [J]. Materials, 2022, 15: 5662.
Crossref Google Scholar
146
WANG R X, CHEUNG C F, WANG C J, et al. Deep learning characterization of surface defects in the selective laser melting process [J]. Computers in Industry, 2022, 140: 103662.
Crossref Google Scholar
147
TRIDELLO A, CIAMPAGLIA A, BERTO F, et al. Assessment of the critical defect in additive manufacturing components through machine learning algorithms [J]. Applied Sciences, 2023, 13 (7): 4294.
Crossref Google Scholar
148
SONG Z R, WANG X M, GAO Y Y, et al. A hybrid deep generative network for pore morphology prediction in metal additive manufacturing [J]. Journal of Manufacturing Science and Engineering, 2023, 145 (7): 071005.
Crossref Google Scholar
149
KWON O, KIM G H, HAM J M, et al. A deep neural network for classification of melt-pool images in metal additive manufacturing [J]. Journal of Intelligent Manufacturing, 2020, 31 (2): 375-386.
Crossref Google Scholar
150
LI B, ZHANG W, XUAN F Z. Machine-learning prediction of selective laser melting additively manufactured part density by feature-dimension-ascended Bayesian network model for process optimisation [J]. The International Journal of Advanced Manufacturing Technology, 2022, 121: 4023-4038.
Crossref Google Scholar
151
MAITRA V, SHI J, LU C Y. Robust prediction and validation of as-built density of Ti-6Al-4V parts manufactured via selective laser melting using a machine learning approach [J]. Journal of Manufacturing Processes, 2022, 78: 183-201.
Crossref Google Scholar
152
RAJU K L, THAPLIYAL S, SIGATAPU S, et al. Process parameter dependent machine learning model for densification prediction of selective laser melted Al-50Si alloy and its validation [J]. Journal of Materials Engineering and Performance, 2022, 31 (10): 8451-8458.
Crossref Google Scholar
153
YANASE Y, MIYAUCHI H, MATSUMOTO H, et al. Densification behavior and microstructures of the Al-10%Si-0.35Mg alloy fabricated by selective laser melting: from experimental observation to machine learning: mechanics of materials [J]. Materials Transactions, 2022, 63 (2): 176-184.
Crossref Google Scholar
154
RANKOUHI B, JAHANI S, PFEFFERKORN F E, et al. Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters [J]. Additive Manufacturing, 2021, 38: 101836.
Crossref Google Scholar
155
LA FÉ-PERDOMO I, RAMOS-GREZ J A, JERIA I, et al. Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting [J]. Journal of Manufacturing Processes, 2022, 80: 666-682.
Crossref Google Scholar
156
MAITRA V, SHI J. Evaluating the predictability of surface roughness of Ti-6Al-4V alloy from selective laser melting [J]. Advanced Engineering Materials, 2023, 25: 2300075.
Crossref Google Scholar
157
FOTOVVATI B, CHOU K. Build surface study of single-layer raster scanning in selective laser melting: surface roughness prediction using deep learning [J]. Manufacturing Letters, 2022, 33: 701-711.
Crossref Google Scholar
158
BARRIONUEVO G O, WALCZAK M, RAMOS-GREZ J, et al. Microhardness and wear resistance in materials manufactured by laser powder bed fusion: machine learning approach for property prediction [J]. CIRP Journal of Manufacturing Science and Technology, 2023, 43: 106-114.
Crossref Google Scholar
159
THEEDA S, JAGDALE H S, RAVICHANDER B B, et al. Optimization of process parameters in laser powder bed fusion of SS 316L parts using artificial neural networks [J]. Metals, 2023, 13 (5): 842.
Crossref Google Scholar
160
YANG Z T, YANG M, SISSON R, et al. Machine learning model to predict tensile properties of annealed Ti6Al4V parts prepared by selective laser melting [J]. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2022, 36: E30.
Crossref Google Scholar
161
KUSANO M, MIYAZAKI S, WATANABE M, et al. Tensile properties prediction by multiple linear regression analysis for selective laser melted and post heat-treated Ti-6Al-4V with microstructural quantification [J]. Materials Science and Engineering A, 2020, 787: 139549.
Crossref Google Scholar
162
MUHAMMAD W, BRAHME A P, IBRAGIMOVA O, et al. A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys [J]. International Journal of Plasticity, 2021, 136: 102867.
Crossref Google Scholar
163
CENTOLA A, CIAMPAGLIA A, TRIDELLO A, et al. Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components [J]. Fatigue and Fracture of Engineering Materials and Structures, 2023, 46 (11): 4350-4370.
Crossref Google Scholar
164
HORŇAS J, BĚHAL J, HOMOLA P, et al. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach [J]. International Journal of Fatigue, 2023, 169: 107483.
Crossref Google Scholar
165
LI J, YANG Z M, QIAN G A, et al. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting [J]. International Journal of Fatigue, 2022, 158: 106764.
Crossref Google Scholar
166
SHI T, SUN J Y, LI J H, et al. Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting [J]. International Journal of Fatigue, 2023, 171: 107585.
Crossref Google Scholar
167
WU J G, AN H P. Yield locus of dispersed shrinkage during densifying process [J]. Foundry, 2020, 69 (12): 1272-1276 (in Chinese).
Google Scholar
168
CHEN D J, WANG P, PAN R, et al. Characteristics of metal specimens formed by selective laser melting: a state-of-the-art review [J]. Journal of Materials Engineering and Performance, 2020, 30 (10): 7073-7100.
Crossref Google Scholar
169
ZHANG J K, KONG X Y, MA S J, et al. Laser additive manufactured high strength-toughness TC11 titanium alloy: Mechanical properties and application in airframe load-bearing structure [J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (10): 525430 (in Chinese).
Google Scholar
170
XIAO G J, LIU S, HE Y, et al. Defocus control and surface topography of titanium alloy laser belt processing [J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (4): 525603 (in Chinese).
Google Scholar
171
PENG Z L, ZHANG X Y, ZHANG D Y. High-speed ultrasonic vibration cutting for difficult-to-machine materials in aerospace field [J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (4): 525587 (in Chinese).
Google Scholar
172
NAGARAJAN B, HU Z H, SONG X, et al. Development of micro selective laser melting: the state of the art and future perspectives [J]. Engineering, 2019, 5 (4): 702-720.
Crossref Google Scholar
173
SUN J F, YANG Y Q, WANG D. Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method [J]. Optics and Laser Technology, 2013, 49: 118-124.
Crossref Google Scholar
174
DO D K, LI P F. The effect of laser energy input on the microstructure, physical and mechanical properties of Ti-6Al-4V alloys by selective laser melting [J]. Virtual and Physical Prototyping, 2016, 11 (1): 41-47.
Crossref Google Scholar
175
XU W, BRANDT M, SUN S, et al. Additive manufacturing of strong and ductile Ti-6Al-4V by selective laser melting via in situ martensite decomposition [J]. Acta Materialia, 2015, 85: 74-84.
Crossref Google Scholar
176
ETTEFAGH A H, ZENG C Y, GUO S M, et al. Corrosion behavior of additively manufactured Ti-6Al-4V parts and the effect of post annealing [J]. Additive Manufacturing, 2019, 28: 252-258.
Crossref Google Scholar
177
ZAFARI A, XIA K. Superior titanium from hybridised microstructures - a new strategy for future alloys [J]. Scripta Materialia, 2019, 173: 61-65.
Crossref Google Scholar
178
PENG B Y, LIU Y, ZHENG X D, et al. Compression resistance of particle reinforced titanium matrix composites prepared by selective laser melting [J]. Journal of Materials Engineering, 2022, 50 (6): 36-48 (in Chinese).
Google Scholar
179
LI N, LIU W, WANG Y, et al. Laser additive manufacturing on metal matrix composites: a review [J]. Chinese Journal of Mechanical Engineering, 2021, 34 (3): 208-223.
Crossref Google Scholar
180
HAYAT M D, SINGH H, HE Z, et al. Titanium metal matrix composites: an overview [J]. Composites Part A: Applied Science and Manufacturing, 2019, 121: 418-438.
Crossref Google Scholar
181
JIANG Q H, LI S, GUO S, et al. Comparative study on process-structure-property relationships of TiC/Ti6Al4V and Ti6Al4V by selective laser melting [J]. International Journal of Mechanical Sciences, 2023, 241: 107963.
Crossref Google Scholar
182
ZHOU Z G, LIU Y Z, LIU X H, et al. Microstructure evolution and mechanical properties of in-situ Ti6Al4V-TiB composites manufactured by selective laser melting [J]. Composites Part B: Engineering, 2021, 207: 108567.
Crossref Google Scholar
183
SHISHKOVSKY I, KAKOVKINA N, SHERBAKOV V. Graded layered titanium composite structures with TiB2 inclusions fabricated by selective laser melting [J]. Composite Structures, 2017, 169: 90-96.
Crossref Google Scholar
184
ZHU L, WU W J, FAN S Q, et al. Microstructure and mechanical properties of in-situ laser additive manufacturing of TiN reinforced Ti6Al4V matrix composites based on gas-liquid reaction [J]. Rare Metal Materials and Engineering, 2022, 51 (6): 2151-2160 (in Chinese).
Google Scholar
185
ZHANG J L, SONG B, CAI C, et al. Tailorable microstructure and mechanical properties of selective laser melted TiB/Ti-6Al-4V composite by heat treatment [J]. Advanced Powder Materials, 2022, 1 (2): 100010.
Crossref Google Scholar
186
TAUB A, MOOR E D, LUO A, et al. Materials for automotive lightweighting [J]. Annual Review of Materials Research, 2019, 49 (1): 327-359.
Crossref Google Scholar
187
ZHAO X, DONG S Y, YAN S X, et al. The effect of different scanning strategies on microstructural evolution to 24CrNiMo alloy steel during direct laser deposition [J]. Materials Science and Engineering A, 2020, 771: 138557.
Crossref Google Scholar
188
CUI X, ZHANG S, WANG Z Y, et al. Microstructure and fatigue behavior of 24CrNiMo low alloy steel prepared by selective laser melting [J]. Materials Science and Engineering A, 2022, 845: 143215.
Crossref Google Scholar
189
WANG F Z, ZHANG C H, CUI X, et al. Effect of energy density on the defects, microstructure, and mechanical properties of selective-laser-melted 24CrNiMo low-alloy steel [J]. Journal of Materials Engineering and Performance, 2022, 31 (5): 3520-3534.
Crossref Google Scholar
190
CHEN Y, RONG P, MEN X N, et al. An experimental investigation into residual stress control of 24CrNiMo alloy steel by selective laser melting [J]. Coatings, 2023, 13 (2): 321.
Crossref Google Scholar
191
MA Y X, GAO Y F, ZHAO L, et al. Optimization of process parameters and analysis of microstructure and properties of 18Ni300 by selective laser melting [J]. Materials, 2022, 15 (14): 4757.
Crossref Google Scholar
192
HABASSI F, HOURIA M, BARKA N, et al. Influence of post-treatment on microstructure and mechanical properties of additively manufactured C300 maraging steel [J]. Materials Characterization, 2023, 202: 112980.
Crossref Google Scholar
193
OSTROVSKI I F, RABELO A, BODZIAK S, et al. Effect of the plasma nitriding on the mechanical properties of the 18Ni300 steel obtained by selective laser melting [J]. Surface and Coatings Technology, 2023, 466: 129688.
Crossref Google Scholar
194
FANG Y J, KIM M K, ZHANG Y L, et al. Particulate-reinforced iron-based metal matrix composites fabricated by selective laser melting: a systematic review [J]. Journal of Manufacturing Processes, 2022, 74: 592-639.
Crossref Google Scholar
195
ZHAO X, WEI Q S, GAO N, et al. Rapid fabrication of TiN/AISI 420 stainless steel composite by selective laser melting additive manufacturing [J]. Journal of Materials Processing Technology, 2019, 270: 8-19.
Crossref Google Scholar
196
TRAN D, LIN C K, TUNG P C, et al. Enhancing mechanical and corrosion properties of AISI 420 with titanium-nitride reinforcement through high-power-density selective laser melting using two-stage mixed TiN/AISI 420 powder [J]. Materials, 2023, 16 (11): 4198.
Crossref Google Scholar
197
OZSOY A, AYDOGAN E, DERICIOGLU A F. Selective laser melting of Nano-TiN reinforced 17-4 PH stainless steel: densification, microstructure and mechanical properties [J]. Materials Science and Engineering A, 2022, 836: 142574.
Crossref Google Scholar
198
KANG N, MA W Y, LI F H, et al. Microstructure and wear properties of selective laser melted WC reinforced 18Ni-300 steel matrix composite [J]. Vacuum, 2018, 154: 69-74.
Crossref Google Scholar
199
ZHANG M H, ZHANG B C, WEN Y J, et al. Research progress on selective laser melting processing for nickel-based superalloy [J]. International Journal of Minerals Metallurgy and Materials, 2022, 29 (3): 369-388.
Crossref Google Scholar
200
SUI S, LI H S, LI Z, et al. Introduction of a new method for regulating laves phases in inconel 718 superalloy during a laser-repairing process [J]. Engineering, 2022, 16 (9): 239-246.
Crossref Google Scholar
201
PANWISAWAS C, TANG Y B, REED R C. Metal 3D printing as a disruptive technology for superalloys [J]. Nature Communications, 2020, 11 (1): 2327.
Crossref Google Scholar
202
MOSTAFAEI A, GHIAASIAAN R, HO I T, et al. Additive manufacturing of nickel-based superalloys: a state-of-the-art review on process-structure-defect-property relationship [J]. Progress in Materials Science, 2023, 136: 101108.
Crossref Google Scholar
203
XU J, WU Z C, NIU J P, et al. Effect of laser energy density on the microstructure and microhardness of inconel 718 alloy fabricated by selective laser melting [J]. Crystals, 2022, 12 (9): 1243.
Crossref Google Scholar
204
BAI P K, HUO P C, WANG J, et al. Microstructural evolution and mechanical properties of Inconel 718 alloy manufactured by selective laser melting after solution and double aging treatments [J]. Journal of Alloys and Compounds, 2022, 911: 164988.
Crossref Google Scholar
205
GAIN A K, LI Z, ZHANG L C. Wear mechanism, subsurface structure and nanomechanical properties of additive manufactured Inconel nickel (IN718) alloy [J]. Wear, 2023, 523: 204863.
Crossref Google Scholar
206
HE L, WANG C, DENG T T. Effect of post treatment process on microstructure and mechanical behaviour of Hastelloy-X alloy produced by selective laser melting [J]. Journal of Chinese Society of Power Engineering, 2023, 43 (5): 511-518 (in Chinese).
Google Scholar
207
LI Y L, LEI L M, HOU H P, et al. Effect of heat processing on microstructures and tensile properties of selective laser melting Hastelloy X alloy [J]. Journal of Materials Engineering, 2019, 47 (5): 100-106 (in Chinese).
Google Scholar
208
MONTERO-SISTIAGA M L, POURBABAK S, HUMBEECK J V, et al. Microstructure and mechanical properties of Hastelloy X produced by HP-SLM (high power selective laser melting) [J]. Materials and Design, 2019, 165: 107598.
Crossref Google Scholar
209
YUAN Z W, CHANG F C, CHEN A, et al. Microstructure and properties of SLM-Hastelloy X alloy after different hot isostatic pressing + heat treatment [J]. Materials Science and Engineering A, 2022, 852: 143714.
Crossref Google Scholar
210
WANG Z Q, GAO S, LI S J, et al. Research progress of laser additive manufacturing nickel-based alloy metal matrix composites [J]. Metals, 2023, 13 (1): 129.
Crossref Google Scholar
211
SHUAI C J, WANG B, BIN S Z, et al. TiO2-induced in situ reaction in graphene oxide-reinforced AZ61 biocomposites to enhance the interfacial bonding [J]. ACS Applied Materials and Interfaces, 2020, 12 (20): 23464-23473.
Crossref Google Scholar
212
LIU S Y, ZHANG W, PENG Y B, et al. Microstructure evolution and mechanical properties of in-situ multi-component carbides reinforced FeCoNi alloy [J]. Journal of Alloys and Compounds, 2021, 886: 161215.
Crossref Google Scholar
213
CHEN W G, YANG T, DONG L L, et al. Advances in graphene reinforced metal matrix nanocomposites: mechanisms, processing, modelling, properties and applications [J]. Nanotechnology and Precision Engineering, 2020, 3 (4): 189-210.
Crossref Google Scholar
214
ZHANG B C, BI G J, NAI S, et al. Microhardness and microstructure evolution of TiB2 reinforced Inconel 625/TiB2 composite produced by selective laser melting [J]. Optics and Laser Technology, 2016, 80: 186-195.
Crossref Google Scholar
215
CHENG X P, ZHAO Y N, QIAN Z, et al. Crack elimination and mechanical properties enhancement in additive manufactured Hastelloy X via in-situ chemical doping of Y2O3 [J]. Materials Science and Engineering A, 2021, 824: 141867.
Crossref Google Scholar
216
GAO Y K, CHEN H S, ZHOU J, et al. Microstructures and wear behaviors of WC particle reinforced nickel-based composites fabricated by selective laser melting [J]. Journal of Manufacturing Processes, 2023, 95: 291-301.
Crossref Google Scholar
217
WANG P, ZHANG B C, TAN C C, et al. Microstructural characteristics and mechanical properties of carbon nanotube reinforced Inconel 625 parts fabricated by selective laser melting [J]. Materials and Design, 2016, 112: 290-299.
Crossref Google Scholar
Acta Aeronautica et Astronautica Sinica
Volume 45 Issue 13,
July 2024
Article number: 629508
DOI: 10.7527/S1000-6893.2023.29508
Cite this article:
ZHOU Y, MA Y, GUAN Y. Research progress on laser selective melting technology for high-performance manufacturing of aero-engines. Acta Aeronautica et Astronautica Sinica, 2024, 45(13): 629508. https://doi.org/10.7527/S1000-6893.2023.29508

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Received: 31 August 2023
Revised: 27 September 2023
Accepted: 20 October 2023
Published: 09 November 2023
© 2024 The Journal of Acta Aeronautica et Astronautica Sinica
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