Skip to main content
SpringerLink
Log in

Process parameter optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The present work focuses on process parameter optimisation for selective laser melting (SLM) of AlSi10Mg-316L multi-materials using machine learning method. The properties of the multi-material samples were measured at different process parameters. These process parameter and property data were used to train and validate the machine learning model. A multi-output Gaussian process regression (MO-GPR) model was developed to directly predict the multidimensional output to overcome the limitations of the standard Gaussian process regression (GPR) model. Based on the prediction data, process parameter maps were constructed, and the optimal process parameters for different compositions were selected from the process parameter maps. The results showed that the laser power, scan velocity and hatching space have an important influence on the density and surface roughness of the samples. Results also indicated that there is no linear functional relationship between the optimal volumetric energy density (VED) values and the AlSi10Mg-316L compositions.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The data used or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The machine learning algorithm used during the current study are available from the corresponding author on reasonable request.

References

  1. Rielli VV, Piglione A, Pham M-S, Primig S (2022) On the detailed morphological and chemical evolution of phases during laser powder bed fusion and common post-processing heat treatments of IN718. Addit Manuf 50:102540. https://doi.org/10.1016/j.addma.2021.102540

    Article  Google Scholar 

  2. Buffa G, Costa A, Palmeri D, Pollara G, Barcellona A, Fratini L (2023) A new control parameter to predict micro-warping-induced job failure in LPBF of TI6AL4V titanium alloy. The Inter J Adv Manuf Technol 126:1143–1157. https://doi.org/10.1007/s00170-023-11179-6

    Article  Google Scholar 

  3. Yamamoto S, Azuma H, Suzuki S, Kajino S, Sato N, Okane T, Nakano S, Shimizu T (2019) Melting and solidification behavior of Ti-6Al-4V powder during selective laser melting. The Inter J Adv Manuf Technol 103:4433–4442. https://doi.org/10.1007/s00170-019-03384-z

    Article  Google Scholar 

  4. Jadhav SD, Goossens LR, Kinds Y, Hooreweder BV, Vanmeensel K (2021) Laser-based powder bed fusion additive manufacturing of pure copper. Addit Manuf 42:101990. https://doi.org/10.1016/j.addma.2021.101990

    Article  Google Scholar 

  5. Zheng M, Wei L, Chen J, Zhang Q, Zhang G, Lin X, Huang W (2021) On the role of energy input in the surface morphology and microstructure during selective laser melting of Inconel 718 alloy. J Mater Res Technol 11:392–403. https://doi.org/10.1016/j.jmrt.2021.01.024

    Article  Google Scholar 

  6. Mukherjee T, DebRoy T (2019) Printability of 316 stainless steel. Sci Technol Weld Join 24:412–419. https://doi.org/10.1080/13621718.2019.1607061

    Article  Google Scholar 

  7. Lu J, Lin X, Kang N, Cao Y, Wang Q, Li J, Zhang L, Huang W (2022) On the Sc induced solidification-heterogeneous microstructure in selective laser melted Al-5Mn alloys. J Mater Process Technol 304:117562. https://doi.org/10.1016/j.jmatprotec.2022.117562

    Article  Google Scholar 

  8. Chen K, Wang C, Hong Q, Wen S, Zhou Y, Yan C, Shi Y (2020) Selective laser melting 316L/CuSn10 multi-materials: processing optimization, interfacial characterization and mechanical property. J Mater Process Technol 283:116701. https://doi.org/10.1016/j.jmatprotec.2020.116701

    Article  Google Scholar 

  9. Ghanavati R, Naffakh-Moosavy H (2021) Additive manufacturing of functionally graded metallic materials: a review of experimental and numerical studies. J Mater Res Technol 13:1628–1664. https://doi.org/10.1016/j.jmrt.2021.05.022

    Article  Google Scholar 

  10. Reichardt A, Shapiro AA, Otis R, Dillon RP, Borgonia JP, McEnerney BW, Hosemann P, Beese AM (2021) Advances in additive manufacturing of metal-based functionally graded materials. Int Mater Rev 66:1–29. https://doi.org/10.1080/09506608.2019.1709354

    Article  Google Scholar 

  11. Liu ZH, Zhang DQ, Sing SL, Chua CK, Loh LE (2014) Interfacial characterization of SLM parts in multi-material processing: metallurgical diffusion between 316L stainless steel and C18400 copper alloy. Mater Charact 94:116–125. https://doi.org/10.1016/j.matchar.2014.05.001

    Article  Google Scholar 

  12. Sing SL, Lam LP, Zhang DQ, Liu ZH, Chua CK (2015) Interfacial characterization of SLM parts in multi-material processing: intermetallic phase formation between AlSi10Mg and C18400 copper alloy. Mater Charact 107:220–227. https://doi.org/10.1016/j.matchar.2015.07.007

    Article  Google Scholar 

  13. Chen J, Yang Y, Song C, Wang D, Wu S, Zhang M (2020) Influence mechanism of process parameters on the interfacial characterization of selective laser melting 316L/CuSn10. Mater Sci Eng A 792. https://doi.org/10.1016/j.msea.2020.139316

  14. Wei C, Sun Z, Chen Q, Liu Z, Li L (2019) Additive manufacturing of horizontal and 3D functionally graded 316L/Cu10Sn components via multiple material selective laser melting. J Manuf Sci Eng, Trans of the ASME 141. https://doi.org/10.1115/1.4043983

  15. Zhang C, Chen F, Huang Z, Jia M, Chen G, Ye Y, Lin Y, Liu W, Chen B, Shen Q, Zhang L, Lavernia EJ (2019) Additive manufacturing of functionally graded materials: a review. Mater Sci Eng A 764:138209. https://doi.org/10.1016/j.msea.2019.138209

    Article  Google Scholar 

  16. Starship. Space X. https://www.spacex.com/vehicles/starship/

  17. Fathi A, Mozaffari A (2012) Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map. J Intell Manuf 25:775–795. https://doi.org/10.1007/s10845-012-0718-6

    Article  Google Scholar 

  18. Kappes B, Moorthy S, Drake D, Geerlings H, Stebner A (2018) Machine learning to optimize additive manufacturing parameters for laser powder bed fusion of inconel 718. In: 9th International symposium on superalloy 718 and derivatives: energy, aerospace, and industrial applications 6:595–627. https://doi.org/10.1007/978-3-319-89480-5_39

  19. Garg A, Tai K (2014) An ensemble approach of machine learning in evaluation of mechanical property of the rapid prototyping fabricated prototype. AMM 575:493–496. https://doi.org/10.4028/www.scientific.net/AMM.575.493

  20. Aoyagi K, Wang H, Sudo H, Chiba A (2019) Simple method to construct process maps for additive manufacturing using a support vector machine. Addit Manuf 27:353–362. https://doi.org/10.1016/j.addma.2019.03.013

    Article  Google Scholar 

  21. 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 101836. https://doi.org/10.1016/j.addma.2021.101836

  22. Spierings AB, Schneider M, Eggenberger R (2011) Comparison of density measurement techniques for additive manufactured metallic parts. Rapid Prototyp J 17:380–386. https://doi.org/10.1108/13552541111156504

    Article  Google Scholar 

  23. Chen Z, Wang B, Gorban AN (2020) Multivariate Gaussian and Student-t process regression for multi-output prediction. Neural Comput & Applic 32:3005–3028. https://doi.org/10.1007/s00521-019-04687-8

    Article  Google Scholar 

  24. Peng Y, Jia C, Song L, Bian Y, Tang H, Cai G, Zhong G (2022) The manufacturing process optimization and the mechanical properties of FeCoCrNi high entropy alloys fabricated by selective laser melting. Intermetallics 145:107557. https://doi.org/10.1016/j.intermet.2022.107557

    Article  Google Scholar 

  25. Xu W, Fu P, Wang N, Yang L, Peng L, Chen J, Ding W (2022) Effects of processing parameters on fabrication defects, microstructure and mechanical properties of additive manufactured Mg–Nd–Zn–Zr alloy by selective laser melting process. J Magnes Alloy. https://doi.org/10.1016/j.jma.2022.07.005

  26. Gu D, Shen Y (2009) Balling phenomena in direct laser sintering of stainless steel powder: metallurgical mechanisms and control methods. Mater Des 30:2903–2910. https://doi.org/10.1016/j.matdes.2009.01.013

    Article  Google Scholar 

  27. Li RD, Liu JH, Shi YS, Wang L, Jiang W (2012) Balling behavior of stainless steel and nickel powder during selective laser melting process. Int J Adv Manuf Technol 59:1025–1035. https://doi.org/10.1007/s00170-011-3566-1

    Article  Google Scholar 

  28. Tan JH, Wong WLE, Dalgarno KW (2017) An overview of powder granulometry on feedstock and part performance in the selective laser melting process. Addit Manuf 18:228–255. https://doi.org/10.1016/j.addma.2017.10.011

    Article  Google Scholar 

  29. Spierings AB, Herres N, Levy G (2011) Influence of the particle size distribution on surface quality and mechanical properties in AM steel parts. Rapid Prototyp J 17:195–202. https://doi.org/10.1108/13552541111124770

    Article  Google Scholar 

  30. Riener K, Albrecht N, Ziegelmeier S, Ramakrishnan R, Haferkamp L, Spierings AB, Leichtfried GJ (2020) Influence of particle size distribution and morphology on the properties of the powder feedstock as well as of AlSi10Mg parts produced by laser powder bed fusion (LPBF). Addit Manuf 34:101286. https://doi.org/10.1016/j.addma.2020.101286

    Article  Google Scholar 

  31. Scaramuccia MG, Demir AG, Caprio L, Tassa O, Previtali B (2020) Development of processing strategies for multigraded selective laser melting of Ti6Al4V and IN718. Powder Technol 367:376–389. https://doi.org/10.1016/j.powtec.2020.04.010

    Article  Google Scholar 

Download references

Funding

This study was carried out with the support of funds from the Universiti Malaya Faculty Research Grant 2023 with grant number GPF002A-2023 and the King Khalid University Small Groups Project with grant number RGP. 1/244/44.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Huan Miao, Farazila Yusof & Mohd Sayuti Ab Karim

  2. Faculty of Mechanical Engineering, Gansu Institute of Mechanical and Electrical Engineering, Tianshui, 741001, China

    Huan Miao & Hao Zhang

  3. Centre of Advanced Manufacturing and Material Processing (AMMP Centre), Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Farazila Yusof & Mohd Sayuti Ab Karim

  4. Centre of Foundation Studies in Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Farazila Yusof

  5. Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha, Asir, 61421, Saudi Arabia

    Irfan Anjum Badruddin & Sarfaraz Kamangar

  6. Department of Chemistry, College of Science, King Khalid University, Abha, Asir, 61413, Saudi Arabia

    Mohamed Hussien

Authors
  1. Huan Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farazila Yusof
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd Sayuti Ab Karim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Irfan Anjum Badruddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohamed Hussien
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sarfaraz Kamangar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the conception and design of the study. Experiments on the fabrication of multi-materials and data collection were carried out by H.M. and H.Z. Data analysis was performed by H.M., I.A.B., M.H. and S.K. The first draught of the manuscript was written by H.M., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. F.Y. and M.S.A.K. are the supervisors. They provided comprehensive advice on the research design, experimental methods, data analysis methods and writing of the manuscript.

Corresponding author

Correspondence to Farazila Yusof.

Ethics declarations

Ethical approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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.

Supplementary information

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miao, H., Yusof, F., Karim, M.S.A. et al. Process parameter optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method. Int J Adv Manuf Technol 129, 3093–3108 (2023). https://doi.org/10.1007/s00170-023-12489-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12489-5

Keywords

Search

Navigation