Skip to main content
SpringerLink
Log in

Rapid Process Modeling of the Aerosol Jet Printing Based on Gaussian Process Regression with Latin Hypercube Sampling

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Aerosol jet printing (AJP) technology is a relatively new 3D printing technology for producing customized microelectronic components due to its high design flexibility and fine feature deposition. However, complex interactions between machine, process parameters and materials will influence line morphology and remain a challenge on modeling effectively. And the system drift which induced by many changing and uncertain factors will affect the printing process significantly. Hence, it is necessary to develop a small data set based machine learning approach to model relationship between the process parameters and the line morphology. In this paper, we propose a rapid process modeling method for AJP process and consider sheath gas flow rate, carrier gas flow rate, stage speed as AJP process parameters, and line width and line roughness as the line morphology. Latin hypercube sampling is adopted to generate experimental points. And, Gaussian process regression (GPR) is used for modeling the AJP process because GPR has the capability of providing the prediction uncertainty in terms of variance. The experimental result shows that the proposed GPR model has competitive modeling accuracy comparing to the other regression models.

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
Fig. 15

Similar content being viewed by others

References

  1. Daniel, J. (2010). Printed electronics: Technologies, challenges, and applications. In International workshop on flexible printed electronics, Muju Resort, Korea, September 8–10, 2010.

  2. Jones, C. S., et al. (2010). Aerosol-jet-printed, high-speed, flexible thin-film transistor made using single-walled carbon nanotube solution. Microelectronic Engineering,87(3), 434–437.

    Article  Google Scholar 

  3. Perez, K. B., & Williams, C. B. (2014). Design considerations for hybridizing additive manufacturing and direct write technologies. In ASME international design engineering technical conferences and computers and information in engineering conference, New York, USA, August 17–20, 2014.

  4. Goth, C., Putzo, S., & Franke, J. (2011). Aerosol jet printing on rapid prototyping materials for fine pitch electronic applications. In Electronic components and technology conference, Florida, USA, May 31–June 3, 2011.

  5. Mahajan, A., Frisbie, C. D., & Francis, L. F. (2013). Optimization of aerosol jet printing for high-resolution, high-aspect ratio silver lines. ACS Applied Materials & Interfaces,5(11), 4856–4864.

    Article  Google Scholar 

  6. Salary, R. R., et al. (2017). Computational fluid dynamics modeling and online monitoring of aerosol jet printing process. Journal of Manufacturing Science and Engineering,139(2), 1–21.

    Article  Google Scholar 

  7. Feng, J. Q. (2016). A computational study of high-speed microdroplet impact onto a smooth solid surface. Journal of Applied Fluid Mechanics,10(1), 1–26.

    Google Scholar 

  8. Vogeler, F., et al. (2013). An initial study into aerosol jet® printed interconnections on extrusion based 3D printed substrates. Journal of Mechanical Engineering,59(11), 689–696.

    Article  Google Scholar 

  9. Wadhwa, A. (2015). Run-time ink stability in pneumatic aerosol jet printing using a split stream solvent add back system. Rochester Institute of Technology, 2015.

  10. Smith, M., et al. (2017). Controlling and assessing the quality of aerosol jet printed features for large area and flexible electronics. Flexible and Printed Electronics,2(1), 2–11.

    Article  Google Scholar 

  11. Verheecke, W. et al. (2012). Optimizing aerosol jet printing of silver interconnects on polyimide film for embedded electronics applications. In Eighth international DAAAM Baltic conference, Tallinn, Estonia, April 19–21, 2012.

  12. Wang, K., et al. (2013). Evaluation of quality of printed strain sensors for composite structural health monitoring applications. In SAMPE fall technical conference, Wichita, USA, October 21–24, 2013.

  13. Christenson, K. K., et al. (2011). Direct printing of circuit boards using aerosol jet ®,” NIP & Digital Fabrication Conference, Minnesota, USA, October 2–6, 2011.

  14. Hedges, M., Marin, A. B. (2012). 3D aerosol jet printing-Adding electronics functionality to RP/RM. In DDMC conference, Berlin, Germany, March 15–16, 2012.

  15. Tait, J. G., et al. (2015). Uniform aerosol jet printed polymer lines with 30 μm width for 140 ppi resolution RGB organic light emitting diodes. Organic Electronics,22(1), 40–43.

    Article  Google Scholar 

  16. Salary, R. R., et al. (2017). Online monitoring of functional electrical properties in aerosol jet printing additive manufacturing process using shape-from-shading image analysis. Journal of Manufacturing Science and Engineering,139(10), 1–13.

    Article  Google Scholar 

  17. Kopola, P., et al. (2012). Aerosol jet printed grid for ITO-free inverted organic solar cells. Solar Energy Materials and Solar Cells,107(1), 252–258.

    Article  Google Scholar 

  18. Schulz, D., et al. (2010). Collimated Aerosol beam deposition: Sub-5 μm resolution of printed actives and passives. IEEE Transactions on Advanced Packaging,33(2), 421–427.

    Article  Google Scholar 

  19. Akhatov, I. S., et al. (2009). Aerosol flow through a micro-capillary. In ASME second international conference on micro/nanoscale heat and mass transfer, Shanghai, China, December 18–21, 2009.

  20. Binder, S., Glatthaar, M., & Rädlein, E. (2014). Analytical investigation of aerosol jet printing. Aerosol Science and Technology,48(9), 924–929.

    Article  Google Scholar 

  21. Feng, J. Q. (2017). A computational study of particle deposition patterns from a circular laminar jet. Journal of Applied Fluid Mechanics,10(4), 1–19.

    Google Scholar 

  22. Rasmussen, C. E., & Williams, C. K. (2006). Gaussian process for machine learning. Berlin, Heidalberg: MIT press.

    MATH  Google Scholar 

  23. Hernández, N., et al. (2008). Relevance vector machines for multivariate calibration purposes. Journal of Chemometrics,22(11–12), 686–694.

    Article  Google Scholar 

  24. Rasmussen, C. E. (1999). Evaluation of Gaussian processes and other methods for non-linear regression. University of Toronto, 1999.

  25. Yuan, J., et al. (2008). Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. International Journal of Machine Tools and Manufacture,48(1), 47–60.

    Article  Google Scholar 

  26. Tang, Q., et al. (2010). Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts. Chemical Engineering Journal,156(2), 423–431.

    Article  Google Scholar 

  27. Chi, G., et al. (2012). Response surface methodology with prediction uncertainty: A multi-objective optimisation approach. Chemical Engineering Research and Design,90(9), 1235–1244.

    Article  Google Scholar 

  28. Fang, K.-T., et al. (2000). Uniform design: Theory and application. Technometrics,42(3), 237–248.

    Article  MathSciNet  MATH  Google Scholar 

  29. Chang, J.-S., & Lin, J.-P. (2004). Product and process development via sequential pseudo-uniform design. Industrial and Engineering Chemistry Research,43(15), 4278–4292.

    Article  Google Scholar 

  30. Kalagnanam, J. R., & Diwekar, U. M. (1997). An efficient sampling technique for off-line quality control. Technometrics,39(3), 308–319.

    Article  MATH  Google Scholar 

  31. McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics,21(2), 239–245.

    MathSciNet  MATH  Google Scholar 

  32. Chen, Y.-L., et al. (2015). Incremental Latin hypercube sampling for lifetime stochastic behavioral modeling of analog circuits. In Design automation conference, Tokyo, January 19–22, 2015.

  33. Yan, S., & Minsker, B. (2006). Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resources Research,42(5), 1–14.

    Article  Google Scholar 

  34. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 1–27.

    Article  Google Scholar 

  35. Mandal, D., Pal, S. K., & Saha, P. (2007). Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. Journal of Materials Processing Technology,186(1–3), 154–162.

    Article  Google Scholar 

Download references

Acknowledgements

This research work was conducted in the SMRT-NTU Smart Urban Rail Corporate Laboratory with funding support from the National Research Foundation (NRF), SMRT and Nanyang Technological University; under the Corp Lab@University Scheme.

Author information

Authors and Affiliations

  1. School of Mechanical and Aerospace, Nanyang Technological University, Singapore, 639798, Singapore

    Haining Zhang & Seung Ki Moon

  2. SMRT Trains Pte Ltd, Singapore, 579828, Singapore

    Teck Hui Ngo, Junjie Tou & Mohamed Ashrof Bin Mohamed Yusoff

Authors
  1. Haining Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seung Ki Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Teck Hui Ngo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junjie Tou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohamed Ashrof Bin Mohamed Yusoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seung Ki Moon.

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

Zhang, H., Moon, S.K., Ngo, T.H. et al. Rapid Process Modeling of the Aerosol Jet Printing Based on Gaussian Process Regression with Latin Hypercube Sampling. Int. J. Precis. Eng. Manuf. 21, 127–136 (2020). https://doi.org/10.1007/s12541-019-00237-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-019-00237-3

Keywords

Search

Navigation