Skip to main content
SpringerLink
Log in

Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks

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

Abstract

Direct energy deposition (DED) is a highly applicable additive manufacturing (AM) method and, therefore, widely employed in industrial repair-based applications to fabricate defect-free and high degree precision components. To obtain high-quality products by using DED, it is necessary to understand the influence of the process parameters on the product quality. The optimization of such processing parameters provides several advantages such as minimization of the loss of material and time. However, the optimization of the complex relationship between the process parameters-geometry-properties of the fabricated sample is difficult to realize and requires significant experimentation. Herein, a computational model based on artificial neural networks was developed to optimize process parameters for DED-processed Ti-6Al-4V alloy. The model was developed to estimate the density and build height (the actual build height realized after fabrication) of the sample as a function of power, scan speed, powder feed rate, and layer thickness. The optimum model with high-accuracy predictions was employed to construct the process maps for the DED-processed Ti-6Al-4V. The relative importance indices of process parameters on the build height and density were investigated. Further, the effect of power and scan speed on the microstructure of Ti-6Al-4V alloy was discussed. Finally, based on the obtained results, the optimum fabrication conditions for the DED-processed Ti-6Al-4V alloy were determined.

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Zenou M, Grainger L (2018) Additive manufacturing of metallic materials. Additive Manufacturing, Elsevier, pp 53–103

    Book  Google Scholar 

  2. Kobryn P, Moore E, Semiatin S (2000) The effect of laser power and traverse speed on microstructure, porosity, and build height in laser-deposited Ti-6Al-4V. Scr Mater 43(4):299–305

    Article  Google Scholar 

  3. Narayana P, Lee S, Choi S-W, Li C-L, Park CH, Yeom J-T, Reddy N, Hong J-K (2019) Microstructural response of β-stabilized Ti–6Al–4V manufactured by direct energy deposition. J Alloys Compd 811:152021

    Article  Google Scholar 

  4. Liu S, Shin YC (2019) Additive manufacturing of Ti6Al4V alloy: A review. Mater Des 164:107552

    Article  Google Scholar 

  5. Khorasani A, Gibson I, Awan US, Ghaderi A (2019) The effect of SLM process parameters on density, hardness, tensile strength and surface quality of Ti-6Al-4V. Additive Manufacturing 25:176–186

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. Majumdar T, Bazin T, Ribeiro EMC, Frith JE, Birbilis N (2019) Understanding the effects of PBF process parameter interplay on Ti-6Al-4V surface properties. PLoS One 14(8):e0221198

    Article  Google Scholar 

  8. Egan DS, Dowling DP (2019) Influence of process parameters on the correlation between in-situ process monitoring data and the mechanical properties of Ti-6Al-4V non-stochastic cellular structures. Additive Manufacturing 30:100890

    Article  Google Scholar 

  9. Levkulich N, Semiatin S, Gockel J, Middendorf J, DeWald A, Klingbeil N (2019) The effect of process parameters on residual stress evolution and distortion in the laser powder bed fusion of Ti-6Al-4V. Additive Manufacturing 28:475–484

    Article  Google Scholar 

  10. Dilip J, Zhang S, Teng C, Zeng K, Robinson C, Pal D, Stucker B (2017) Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting. Progress in Additive Manufacturing 2(3):157–167

    Article  Google Scholar 

  11. Corbin DJ, Nassar AR, Reutzel EW, Beese AM, Kistler NA (2017) Effect of directed energy deposition processing parameters on laser deposited Inconel® 718: external morphology. Journal of Laser Applications 29(2):022001

    Article  Google Scholar 

  12. Wang Z, Palmer TA, Beese AM (2016) Effect of processing parameters on microstructure and tensile properties of austenitic stainless steel 304L made by directed energy deposition additive manufacturing. Acta Mater 110:226–235

    Article  Google Scholar 

  13. Kistler NA, Corbin DJ, Nassar AR, Reutzel EW, Beese AM (2019) Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition. J Mater Process Technol 264:172–181

    Article  Google Scholar 

  14. Dingal S, Pradhan T, Sundar JS, Choudhury AR, Roy S (2008) The application of Taguchi’s method in the experimental investigation of the laser sintering process. Int J Adv Manuf Technol 38(9-10):904–914

    Article  Google Scholar 

  15. Sun J, Yang Y, Wang D (2013) Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method. Opt Laser Technol 49:118–124

    Article  Google Scholar 

  16. Aslani K-E, Kitsakis K, Kechagias JD, Vaxevanidis NM, Manolakos DE (2020) On the application of grey Taguchi method for benchmarking the dimensional accuracy of the PLA fused filament fabrication process. SN Applied Sciences 2(6):1016

    Article  Google Scholar 

  17. Aslani K, Vakouftsi F, Kechagias JD, Mastorakis NE (2019) Surface roughness optimization of Poly-Jet 3D printing using grey Taguchi method, 2019 International Conference on Control. Artificial Intelligence, Robotics & Optimization (ICCAIRO), pp 213–218

    Google Scholar 

  18. Kechagias J (2007) Investigation of LOM process quality using design of experiments approach. Rapid Prototyp J 13(5):316–323

    Article  Google Scholar 

  19. Bartolomeu F, Faria S, Carvalho O, Pinto E, Alves N, Silva FS, Miranda G (2016) Predictive models for physical and mechanical properties of Ti6Al4V produced by Selective Laser Melting. Mater Sci Eng A 663:181–192

    Article  Google Scholar 

  20. Menon A, Póczos B, Feinberg AW, Washburn NR (2019) optimization of silicone 3D printing with hierarchical machine learning, 3D Printing and Additive. Manufacturing 6(4):181–189

    Google Scholar 

  21. 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. Additive Manufacturing 21:517–528

    Article  Google Scholar 

  22. 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. Additive Manufacturing 27:42–53

    Article  Google Scholar 

  23. 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. Additive Manufacturing 25:151–165

    Article  Google Scholar 

  24. Qi X, Chen G, Li Y, Cheng X, Li C (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5(4):721–729

    Article  Google Scholar 

  25. Yang W, Calius E, L. Huang, S. Singamneni (2020) Artificial evolution and design for multi-material additive manufacturing, 3D Printing and Additive Manufacturing.

  26. Kechagias J, Iakovakis V (2009) A neural network solution for LOM process performance. Int J Adv Manuf Technol 43(11):1214–1222

    Article  Google Scholar 

  27. Fountas NA, Kechagias JD, Tsiolikas AC, Vaxevanidis NM (2020) Multi-objective optimization of printing time and shape accuracy for FDM-fabricated. ABS parts, 1 1(2):115

    Google Scholar 

  28. Marrey M, Malekipour E, El-Mounayri H, Faierson EJ (2019) A framework for optimizing process parameters in powder bed fusion (PBF) process using artificial neural network (ANN). Procedia Manufacturing 34:505–515

    Article  Google Scholar 

  29. Jiang J, Hu G, Li X, Xu X, Zheng P, Stringer J (2019) Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network. Virtual and Physical Prototyping 14(3):253–266

    Article  Google Scholar 

  30. Karnik S, Gaitonde V (2008) Development of artificial neural network models to study the effect of process parameters on burr size in drilling. Int J Adv Manuf Technol 39(5):439–453

    Article  Google Scholar 

  31. Akinlabi ET, Akinlabi SA (2016) Advanced coating: laser metal deposition of aluminium powder on titanium substrate, Proceedings of the World Congress on Engineering.

  32. Sobiyi K, Akinlabi ET, Akinlabi SA (2017) The influence of scanning speed on laser metal deposition of Ti/TiC powders.

  33. Pityana S, Mahamood RM, Akinlabi ET, Shukla M (2013) Gas flow rate and powder flow rate effect on properties of laser metal deposited Ti6Al4V.

  34. Zhang B, Li Y, Bai Q (2017) Defect formation mechanisms in selective laser melting: a review. Chinese Journal of Mechanical Engineering 30(3):515–527

    Article  Google Scholar 

  35. Louw DF, Pistorius P (2019) The effect of scan speed and hatch distance on prior-beta grain size in laser powder bed fused Ti-6Al-4V. Int J Adv Manuf Technol 103(5-8):2277–2286

    Article  Google Scholar 

  36. Liu J, Song Y, Chen C, Wang X, Li H, Wang J, Guo K, Sun J (2020) Effect of scanning speed on the microstructure and mechanical behavior of 316L stainless steel fabricated by selective laser melting. Mater Des 186:108355

    Article  Google Scholar 

  37. Köhnen P, Letang M, Voshage M, Schleifenbaum JH, Haase C (2019) Understanding the process-microstructure correlations for tailoring the mechanical properties of L-PBF produced austenitic advanced high strength steel. Additive Manufacturing 30:100914

    Article  Google Scholar 

  38. Kusuma C (2016) The effect of laser power and scan speed on melt pool characteristics of pure titanium and Ti-6Al-4V alloy for selective laser melting.

  39. Park CH, Cha D, Kim M, Reddy NS, Yeom J-T (2019) Neural network approach to construct a processing map from a non-linear stress–temperature relationship. Met Mater Int 25(3):768–778

    Article  Google Scholar 

  40. Narayana PL, Kim S-W, Hong J-K, Reddy NS, Yeom J-T (2018) Estimation of transformation temperatures in Ti–Ni–Pd shape memory alloys. Met Mater Int 24(5):919–925

    Article  Google Scholar 

  41. Sha W, Malinov S (2009) Titanium alloys: modelling of microstructure, properties and applications, Elsevier.

  42. Reddy N, Panigrahi BB, Ho CM, Kim JH, Lee CS (2015) Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys. Comput Mater Sci 107:175–183

    Article  Google Scholar 

  43. Reddy NS, Krishnaiah J, Hong S-G, Lee JS (2009) Modeling medium carbon steels by using artificial neural networks. Mater Sci Eng A 508(1):93–105

    Article  Google Scholar 

  44. Zhang L, Gao Z, He B, Ni X, Long Q, Lu L, Zhu G (2019) Effect of processing parameters on thermal behavior and related density in GH3536 alloy manufactured by selective laser melting. J Mater Res 34(8):1405–1414

    Article  Google Scholar 

  45. Narayana PL, Lee SW, Park CH, Yeom J-T, Hong J-K, Maurya AK, Reddy NS (2020) Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks. Comput Mater Sci 179:109617

    Article  Google Scholar 

Download references

Availability of data and material

The authors confirm that the data supporting the findings of this study are available within the article.

Code availability

The software used during the current study is available from the corresponding author on reasonable request.

Funding

This study was supported by the Ministry of Trade, Industry, and Energy (Grant Nos. 10077677, 20013202, and 16-CM-MA-10).

Author information

Authors and Affiliations

  1. Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, Korea

    Pasupuleti Lakshmi Narayana, Jae Hyeok Kim, Jaehyun Lee, Seong-Woo Choi, Sangwon Lee, Chan Hee Park, Jong-Taek Yeom & Jae-Keun Hong

  2. School of Materials Science and Engineering, Gyeongsang National University, Jinju, 52828, Korea

    Pasupuleti Lakshmi Narayana & Nagireddy Gari Subba Reddy

Authors
  1. Pasupuleti Lakshmi Narayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae Hyeok Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaehyun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seong-Woo Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sangwon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chan Hee Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jong-Taek Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nagireddy Gari Subba Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jae-Keun Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jae Hyeok Kim or Jae-Keun Hong.

Ethics declarations

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

Narayana, P.L., Kim, J.H., Lee, J. et al. Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks. Int J Adv Manuf Technol 114, 3269–3283 (2021). https://doi.org/10.1007/s00170-021-07115-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-07115-1

Keywords

Search

Navigation