Skip to main content
SpringerLink
Log in

Modeling and simulation of metal selective laser melting process: a critical review

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

Abstract

As a technology of additive manufacturing (AM), selective laser melting (SLM) is widely used in metal printing, such as super-alloys, stainless steel. Also, the SLM is considered the most potential metal additive manufacturing technology. It is difficult to build a process-performance relationship using traditional physical model, since SLM process is multi parameter and multi-scale. Also, the experimental method takes a long time and is expensive, which requires proposal of a new accurate analytical model and simulation method. The uniqueness of this review includes discussions on the model and simulation model based on physical theory and data driven. The analytical model based on physical model in SLM is discussed. In order to solve the nonlinear equation in the physical model, the numerical method that used FEM software is summarized. With the development of machine learning method, the machine learning based on data driven is used in SLM process optimization. The discussion and future trends of three methods are proposed in order to solve the gap between laboratory and industry.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Availability of data and material

All data generated or analyzed during this study are included in this published article.

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Meier C, Penny RW, Zou Y et al (2017) Thermophysical phenomena in metal additive manufacturing by selective laser melting: fundamentals, modeling, simulation and experimentation. Annual Review of Heat Transfer 10:1615–1673. https://doi.org/10.1615/AnnualRevHeatTransfer.2018019042

    Article  Google Scholar 

  4. Papazoglou EL, Karkalos NE, Karmiris-Obratański P, Markopoulos AP (2022) On the modeling and simulation of SLM and SLS for metal and polymer powders: a review. Arch Computat Methods Eng 29:941–973. https://doi.org/10.1007/s11831-021-09601-x

    Article  Google Scholar 

  5. Cooke S, Ahmadi K, Willerth S, Herring R (2020) Metal additive manufacturing: technology, metallurgy and modelling. J Manuf Process 57:978–1003. https://doi.org/10.1016/j.jmapro.2020.07.025

    Article  Google Scholar 

  6. Panwisawas C, Tang YT, Reed RC (2020) Metal 3D printing as a disruptive technology for superalloys. Nat Commun 11(1):2327. https://doi.org/10.1038/s41467-020-16188-7

    Article  Google Scholar 

  7. Shipley H, McDonnell D, Culleton M, Coull R, Lupoi R, O’Donnell G, Trimble D (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.2018.01.003

    Article  Google Scholar 

  8. 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/s00170-021-06810-3

    Article  Google Scholar 

  9. Kotadia HR, Gibbons G, Das A, Howes PD (2021) A review of laser powder bed fusion additive manufacturing of aluminium alloys: microstructure and properties. Addit Manuf 46:102155. https://doi.org/10.1016/j.addma.2021.102155

    Article  Google Scholar 

  10. Grasso M, Colosimo BM (2017) Process defects and in-situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol 28(4):1–25. https://doi.org/10.1088/1361-6501/aa5c4f

    Article  Google Scholar 

  11. Zhang C, Zhu JK, Zheng H (2020) A review on microstructures and properties of high entropy alloys manufactured by selective laser melting. Meas Int J Extrem Manuf 2:032003

    Article  Google Scholar 

  12. Hu Z, Mahadevan S (2017) Uncertainty quantification in prediction of material properties during additive manufacturing. Scripta Mater 135:135–140. https://doi.org/10.1016/j.scriptamat.2016.10.014

    Article  Google Scholar 

  13. Zhang Z, Huang Y, Kasinathan AR 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.2018.08.012

    Article  Google Scholar 

  14. Mirkoohi E, Seivers DE, Garamestani H, Liang SY (2019) Heat source modeling in selective laser melting. Materials 12:2052. https://doi.org/10.3390/ma12132052

    Article  Google Scholar 

  15. Francois MM, Sun A, King WE, Henson NJ, Tourret D, Bronkhorst CA et al (2017) Modeling of additive manufacturing processes for metals: challenges and opportunities. Curr Opin Solid State Mater Sci. https://doi.org/10.1016/j.cossms.2016.12.001

    Article  Google Scholar 

  16. Prashanth KG (2020) Selective laser melting: materials and applications. J Manuf Mater Process 4(1):13. https://doi.org/10.3390/jmmp4010013

    Article  MathSciNet  Google Scholar 

  17. Gunasekaran J, Sevvel P, John Solomon I (2020) Metallic materials fabrication by selective laser melting: a review. Mater Today Proc 37:252–256. https://doi.org/10.1016/j.matpr.2020.05.162

    Article  Google Scholar 

  18. Jiang X, Ye T, Zhu Y (2020) Effect of process parameters on residual stress in selective laser melting of AlSi10Mg. Mater Sci Technol 36(3):342–352. https://doi.org/10.1080/02670836.2019.1705560

    Article  Google Scholar 

  19. Waddell M, Walker K, Bandyopadhyay R, Kapoor K, Sangid MD (2020) Small fatigue crack growth behavior of ti-6al-4v produced via selective laser melting: in situ characterization of a 3d crack tip interactions with defects. Int J Fatigue 137:105638. https://doi.org/10.1016/j.ijfatigue.2020.105638

  20. Fogliatto AAB, Ahrens CH, Wendhausen PAP, Santos EC, Rodrigues D (2020) Correlation between porosity and permeability of stainless steel filters with gradient porosity produced by SLS/SLM. Rapid Prototyp J 26(1):73–81. https://doi.org/10.1108/rpj-09-2018-0224

    Article  Google Scholar 

  21. Tomanek LB, Stutts DS, Pan T, Liou F (2021) Influence of porosity on the thermal, electrical, and mechanical performance of selective laser melted stainless steel. Addit Manuf 39(1–2):101886. https://doi.org/10.1016/j.addma.2021.101886

    Article  Google Scholar 

  22. Yang Y, Gu D, Dai D, Ma C (2018) Laser energy absorption behavior of powder particles using ray tracing method during selective laser melting additive manufacturing of aluminum alloy. Mater Des 143:12–19. https://doi.org/10.1016/j.matdes.2018.01.043

    Article  Google Scholar 

  23. Liu B, Fang G, Lei L (2020) An analytical model for rapid predicting molten pool geometry of selective laser melting (SLM). Appl Math Model 92:505–524. https://doi.org/10.1016/j.apm.2020.11.027

    Article  Google Scholar 

  24. Tan P, Shen F, Li B, Zhou K (2019) A thermo-metallurgical-mechanical model for selective laser melting of Ti6Al4V. Mater Des 189:107642. https://doi.org/10.1016/j.matdes.2019.107642

    Article  Google Scholar 

  25. Tan P, Kiran R, Zhou K (2021) Effects of sub-atmospheric pressure on keyhole dynamics and porosity in products fabricated by selective laser melting. J Manuf Process 64:816–827. https://doi.org/10.1016/j.jmapro.2021.01.058

    Article  Google Scholar 

  26. Ning J, Mirkoohi E, Dong Y, Sievers DE, Garmestani H, Liang SY (2019) Analytical modeling of 3D temperature distribution in selective laser melting of Ti-6Al-4V considering part boundary conditions. J Manuf Process 44:319–326. https://doi.org/10.1016/j.jmapro.2019.06.013

    Article  Google Scholar 

  27. Fergani O, Berto F, Welo T, Liang SY (2016) Analytical modelling of residual stress in additive manufacturing. Fatigue Fract Eng Mater Struct 40(6):971–978. https://doi.org/10.1111/ffe.12560

    Article  Google Scholar 

  28. Panda BK, Sahoo S (2019) Thermo-mechanical modeling and validation of stress field during laser powder bed fusion of AlSi10Mg built part. Results Phys 12:1372–1381. https://doi.org/10.1016/j.rinp.2019.01.002

    Article  Google Scholar 

  29. Mukherjee T, Zhang W, DebRoy T (2017) An improved prediction of residual stresses and distortion in additive manufacturing. Comput Mater Sci 126:360–372. https://doi.org/10.1016/j.commatsci.2016.10.003

    Article  Google Scholar 

  30. Gu D, He B (2016) Finite element simulation and experimental investigation of residual stresses in selective laser melted Ti–Ni shape memory alloy. Comput Mater Sci 117:221–232. https://doi.org/10.1016/j.commatsci.2016.01.044

    Article  Google Scholar 

  31. Huang Y, Yang LJ, Du XZ, Yang YP (2016) Finite element analysis of thermal behavior of metal powder during selective laser melting. Int J Therm Sci 104:146–157. https://doi.org/10.1016/j.ijthermalsci.2016.01.007

    Article  Google Scholar 

  32. Wu J, Wang L, An X (2017) Numerical analysis of residual stress evolution of AlSi10Mg manufactured by selective laser melting. Optik - I J Light Electr Opt 65–78. https://doi.org/10.1016/j.ijleo.2017.02.060

    Article  Google Scholar 

  33. Li Y, Zhou K, Tan P, Tor SB, Chua CK, Leong KF (2018) Modeling temperature and residual stress fields in selective laser melting. Int J Mech Sci 136:24–35. https://doi.org/10.1016/j.ijmecsci.2017.12.001

    Article  Google Scholar 

  34. Luo C, Qiu J, Yan Y, Yang J, Uher C, Tang X (2018) Finite element analysis of temperature and stress fields during the selective laser melting process of thermoelectric Sn Te. J Mater Process Technol 261:74–85

    Article  Google Scholar 

  35. Yu T, Li M, Breaux A, Atri M, Obeidat S, Ma C (2019) Experimental and numerical study on residual stress and geometric distortion in powder bed fusion process. J Manuf Process 46:214–224. https://doi.org/10.1016/j.jmapro.2019.09.010

    Article  Google Scholar 

  36. Stavropoulos P, Foteinopoulos P, Papacharalampopoulos A, Tsoukantas G (2019) Warping in SLM additive manufacturing processes: estimation through thermo-mechanical analysis. Int J Adv Manuf Technol 104:1571–1580. https://doi.org/10.1007/s00170-019-04105-2

    Article  Google Scholar 

  37. Chang CS, Wu KT, Han CF, Tsai TW, Lin JF (2021) Establishment of the model widely valid for the melting and vaporization zones in selective laser melting printings via experimental verifications. Int J Precis Eng Manuf-Green Techno 9:143–162. https://doi.org/10.1007/s40684-020-00283-7

    Article  Google Scholar 

  38. Anand N, Chang KC, Huang PC, Yeh AC, Chen YB (2021) An effective and efficient model for temperature and molding appearance analyses for selective laser melting process. J Mater Process Technol 11709. https://doi.org/10.1016/j.jmatprotec.2021.117109

    Article  Google Scholar 

  39. Ge W, Han S, Na SJ, Fuh JYH (2021) Numerical modelling of surface morphology in selective laser melting. Comput Mater Sci 186:110062. https://doi.org/10.1016/j.commatsci.2020.110062

    Article  Google Scholar 

  40. Yu T, Zhao J (2021) Semi-coupled resolved cfd-dem simulation of powder-based selective laser melting for additive manufacturing. Comput Methods Appl Mech Eng 377:113707. https://doi.org/10.1016/j.cma.2021.113707

    Article  MathSciNet  Google Scholar 

  41. Lindroos M, Pinomaa T, Antikainen A, Lagerbom J, Laukkanen A (2021) Micromechanical modeling approach to single track deformation, phase transformation and residual stress evolution during selective laser melting using crystal plasticity. Addit Manuf 38:101819. https://doi.org/10.1016/j.addma.2020.101819

    Article  Google Scholar 

  42. Vo TQ, Kim BH (2017) Molecular dynamics study of thermodynamic properties of nanoclusters for additive manufacturing. Int J Precis Eng Manuf-Green Technol 4(3):301–306

    Article  Google Scholar 

  43. Ali H, Ghadbeigi H, Mumtaz K (2018) Residual stress development in selective laser-melted Ti6Al4V: a parametric thermal modelling approach. Int J Adv Manuf Technol 97(5–8):2621–2633. https://doi.org/10.1007/s00170-018-2104-9

    Article  Google Scholar 

  44. Krzyzanowski M, Svyetlichnyy D (2022) A multiphysics simulation approach to selective laser melting modelling based on cellular automata and lattice Boltzmann methods. Comput Part Mech 9:117–133. https://doi.org/10.1007/s40571-021-00397-y

    Article  Google Scholar 

  45. Xiang Y, Zhang S, Wei Z, Li J, Wei P, Chen Z, Jiang L (2018) Forming and defect analysis for single track scanning in selective laser melting of Ti6Al4V. Appl Phys A 124(10):685. https://doi.org/10.1007/s00339-018-2056-9

    Article  Google Scholar 

  46. Kundakcıoğlu E, Lazoglu I, Poyraz Ö, Yasa E, Cizicioğlu N (2018) Thermal and molten pool model in selective laser melting process of Inconel 625. Int J Adv Manuf Technol 95(9–12):3977–3984. https://doi.org/10.1007/s00170-017-1489-1

    Article  Google Scholar 

  47. Zou S, Xiao H, Ye F, Li Z, Tang W, Zhu F, Zhu C (2020) Numerical analysis of the effect of the scan strategy on the residual stress in the multi-laser selective laser melting. Res Phys 16:103005. https://doi.org/10.1016/j.rinp.2020.103005

    Article  Google Scholar 

  48. Panwisawas C, Qiu C, Anderson MJ, Sovani Y, Turner RP, Attallah MM, Basoalto HC (2017) Mesoscale modelling of selective laser melting: thermal fluid dynamics and microstructural evolution. Comput Mater Sci 126:479–490. https://doi.org/10.1016/j.commatsci.2016.10.011

    Article  Google Scholar 

  49. Ansari MJ, Nguyen D-S, Park HS (2019) Investigation of SLM process in terms of temperature distribution and melting pool size: modeling and experimental approaches. Materials (Basel) 12(8):1272. https://doi.org/10.3390/ma12081272

    Article  Google Scholar 

  50. Zhang T, Li H, Liu S, Shen S, Xie H, Shi WX, Wei M (2018) Evolution of molten pool during selective laser melting of Ti-6Al-4V. J Phys D Appl Phys 52(2):055302. https://doi.org/10.1088/1361-6463/aaee04

    Article  Google Scholar 

  51. Chen C, Yin J, Zhu H, Xiao Z, Zhang L, Zeng X (2019) Effect of overlap rate and pattern on residual stress in selective laser melting. Int J Mach Tools Manuf 145:103433. https://doi.org/10.1016/j.ijmachtools.2019.103433

    Article  Google Scholar 

  52. Kten K, Biyikolu A (2021) Development of thermal model for the determination of SLM process parameters. Opt Laser Technol 137:106825. https://doi.org/10.1016/j.optlastec.2020.106825

    Article  Google Scholar 

  53. Zhang L, Zhang S, Zhu H (2021) Effect of scanning strategy on geometric accuracy of the circle structure fabricated by selective laser melting. J Manuf Process 64(1):907–915. https://doi.org/10.1016/j.jmapro.2021.02.015

    Article  Google Scholar 

  54. Criales LE, Arısoy YM, Özel T (2016) Sensitivity analysis of material and process parameters in finite element modeling of selective laser melting of Inconel 625. Int J Adv Manuf Technol 86(9–12):2653–2666. https://doi.org/10.1007/s00170-015-8329-y

    Article  Google Scholar 

  55. Bruna-Rosso C, Demir AG, Vedani M, Previtali B (2018) Global sensitivity analyses of a selective laser melting finite element model: influential parameters identification. Int J Adv Manuf Technol 99(1):833–843. https://doi.org/10.1007/s00170-018-2531-7

    Article  Google Scholar 

  56. Han J, Wu M, Ge Y, Wu J (2018) Optimizing the structure accuracy by changing the scanning strategy using selective laser melting. Int J Adv Manuf Technol 95(9–12):4439–4447. https://doi.org/10.1007/s00170-017-1503-7

    Article  Google Scholar 

  57. Scime L, Beuth J (2018) A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Addit Manuf 24:273–286. https://doi.org/10.1016/j.addma.2018.09.034

    Article  Google Scholar 

  58. Kwon O, Kim HG, Ham MJ, Kim W, Kim GH, Cho JH, Kim NI, Kim K (2018) A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf 31:375–386. https://doi.org/10.1007/s10845-018-1451-6

    Article  Google Scholar 

  59. Yuan B, Giera B, Guss G, Matthews M, McMains S (2019) Semi supervised convolutional neural networks for in-situ video monitoring of selective laser melting. IEEE Winter Conf Appl Comput Vis (WACV). https://doi.org/10.1109/WACV.2019.00084

    Article  Google Scholar 

  60. Yang D, Li H, Liu S, Song C, Yang Y, Shen S, Lu J, Liu Z, Zhu Y (2020) In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm. Opt Laser Technol 131:106371. https://doi.org/10.1016/j.optlastec.2020.106371

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  63. Ye DS, Hong GS, Zhang YJ, Zhu KP, Fuh JYH (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/s00170-018-1728-0

    Article  Google Scholar 

  64. Rankouhi B, Jahani S, Pfefferkorn FE, Dan JT (2021) Compositional grading of a 316l-cu multi-material part using machine learning for the determination of selective laser melting process parameters. Addit Manuf 38:101836. https://doi.org/10.1016/j.addma.2021.101836

    Article  Google Scholar 

  65. Li J, Jiexiang Hu, Cao L, Wang S, Liu H, Zhou Qi (2021) Multi-objective process parameters optimization of SLM using the ensemble of metamodels. J Manuf Process 68:198–209. https://doi.org/10.1016/j.jmapro.2021.05.038

    Article  Google Scholar 

  66. Caggiano A, Zhang J, Alfieri V, Caiazzo F, Gao R, Teti R (2019) Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann 68(1):451–454. https://doi.org/10.1016/j.cirp.2019.03.021

    Article  Google Scholar 

  67. Yao B, Imani F, Yang H (2018) Markov decision process for image-guided additive manufacturing. IEEE Robotics and Automation Letters 3(4):2792–2798. https://doi.org/10.1109/lra.2018.2839973

    Article  Google Scholar 

  68. Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf 27:42–53. https://doi.org/10.1016/j.addma.2019.01.006

    Article  Google Scholar 

  69. Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S (2018) Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf 21:517–528. https://doi.org/10.1016/j.addma.2018.04.005

    Article  Google Scholar 

  70. 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/s10845-018-1412-0

    Article  Google Scholar 

  71. Baumgartl H, Tomas J, Buettner R, Merkel M (2020) A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Prog Addit Manuf 5:277–285. https://doi.org/10.1007/s40964-019-00108-3

    Article  Google Scholar 

  72. Scime L, Beuth J (2019) 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 25:151–165. https://doi.org/10.1016/j.addma.2018.11.010

    Article  Google Scholar 

  73. 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.2017.11.009

    Article  Google Scholar 

  74. Park HS, Nguyen DS, Le-Hong T, Van Tran X (2021) Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. J Intell Manuf 1–16. https://doi.org/10.1007/s10845-021-01773-4

    Article  Google Scholar 

  75. Yadav P, Rigo O, Arvieu C, Le Guen E, Lacoste E (2020) Drift detection in selective laser melting (SLM) using a machine learning approach. Ind Addit Manuf 177–191. https://doi.org/10.1007/978-3-030-54334-1_13

    Article  Google Scholar 

  76. Uhlmann E, Pontes RP, Laghmouchi A, Bergmann A (2017) Intelligent pattern recognition of a slm machine process and sensor data. Procedia Cirp 62:464–469. https://doi.org/10.1016/j.procir.2016.06.060

    Article  Google Scholar 

  77. Chen Y, Wang H, Wu Y, Wang H (2020) Predicting the printability in selective laser melting with a supervised machine learning method. Materials 13(22):5063. https://doi.org/10.3390/ma13225063

    Article  Google Scholar 

  78. Yadav P, Rigo O, Arvieu C, Le Guen E, Lacoste E (2020) In situ monitoring systems of the SLM process: on the need to develop machine learning models for data processing. Crystals 10(6):524. https://doi.org/10.3390/cryst10060524

    Article  Google Scholar 

  79. Delli U, Chang S (2018) Automated process monitoring in 3d printing using supervised machine learning. Procedia Manuf 26:865–870. https://doi.org/10.1016/j.promfg.2018.07.111

    Article  Google Scholar 

  80. Liu Q, Wu H, Paul MJ, He P, Peng Z, Gludovatz B et al (2020) Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: new microstructure description indices and fracture mechanisms. Acta Mater 201:316–328. https://doi.org/10.1016/j.actamat.2020.10.010

    Article  Google Scholar 

Download references

Funding

This study is supported by 2021 Xiangyang science and technology plan project in high tech field (2021ABH003929).

Author information

Authors and Affiliations

  1. Xiangyang Key Laboratory of Intelligent Manufacturing and Machine Vision, School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China

    Ruihu Zhou & Haisheng Liu

  2. Hubei Sanwei Lingzao Technology Co., Ltd, Xiangyang, 441003, China

    Huifu Wang

Authors
  1. Ruihu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haisheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huifu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Authorship specific contributions: RZ: literature preparation and writing; HL, HW: review and editing. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ruihu Zhou.

Ethics declarations

Consent to participate

The authors declare that all authors have read and approved to submit this manuscript to IJAMT.

Consent for publication

The authors declare that all authors agree to sign the transfer of copyright for the publisher to publish this article upon on acceptance.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, R., Liu, H. & Wang, H. Modeling and simulation of metal selective laser melting process: a critical review. Int J Adv Manuf Technol 121, 5693–5706 (2022). https://doi.org/10.1007/s00170-022-09721-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09721-z

Keywords

Search

Navigation