Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network

Abstract

In the fabrication of electronic devices, uniform and good quality conductive printed lines are highly desirable. The goal of the present study is to develop a predictive model for conductive pattern lines produced by the microdispensing system. For this purpose, an artificial neural network (ANN) based on a feed-forward backpropagation algorithm is adopted. Input process parameters are pressure, feed rate, and standoff distance, while the output performance parameter (response) is the width of pattern lines generated through 200 µm and 500 µm nozzles diameter. The dispensing material is carbon paste having a viscosity of 30 Pa s. Best levels of process parameters are identified to achieve lower width of pattern lines based on the Taguchi signal-to-noise ratios. The identified best levels are found valid in the ranges of printing process parameters after training the neural networks. The prediction ability of ANN models is evaluated based on the leave-one-out cross-validation technique. The results showed that the proposed ANN model accomplished better results in predicting the width of pattern lines. In addition, the proposed approach is extendable to different materials with a variety of viscosities as well as to other similar printing techniques.

This is a preview of subscription content, access via your institution.

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

References

  1. 1.

    Ru, C.; Luo, J.; Xie, S.; Sun, Y.: A review of non-contact micro-and nano-printing technologies. J. Micromech. Microeng. 24, 53001 (2014)

    Article  Google Scholar 

  2. 2.

    Meiser, I.; Shirley, S.G.; Zimmermann, H.: Kinetic masks: a new approach and device for dispersing biologically relevant fluids. Microsyst. Technol. 15, 1407–1416 (2009)

    Article  Google Scholar 

  3. 3.

    Jackson, N.; Buckley, J.; Clarke, C.; Stam, F.: Manufacturing methods of stretchable liquid metal-based antenna. Microsyst. Technol. 25, 3175–3184 (2019)

    Article  Google Scholar 

  4. 4.

    Zhang, J.; Brazis, P.; Chowdhuri, A.R.; Szczech, J.; Gamota, D.: Investigation of using contact and non-contact printing technologies for organic transistor fabrication. MRS Online Proc. Libr. Arch. 725, P6.3.1–P6.3.6 (2002)

    Google Scholar 

  5. 5.

    Tung, D.T.; Tam, L.T.T.; Dung, H.T.; Dung, N.T.; Ha, H.T.; Dung, N.T.; Hoang, T.; Lam, T.D.; Thu, T.; Chien, D.T.: Direct ink writing of graphene–cobalt ferrite hybrid nanomaterial for supercapacitor electrodes. J. Electron. Mater. 49, 4671–4679 (2020)

    Article  Google Scholar 

  6. 6.

    Abas, M.; Rahman, K.: Fabrication of flex sensors through direct ink write technique and its electrical characterization. Appl. Phys. A Mater. Sci. Process. (2016). https://doi.org/10.1007/s00339-016-0507-8

    Article  Google Scholar 

  7. 7.

    Liu, D.; Ren, J.; Wang, J.; Xing, W.; Qian, Q.; Chen, H.; Zhou, N.: Customizable and stretchable fibre-shaped electroluminescent devices via mulitcore-shell direct ink writing. J. Mater. Chem. C 8, 15092–15098 (2020)

    Article  Google Scholar 

  8. 8.

    Chen, X.B.: Modeling and control of fluid dispensing processes: a state-of-the-art review. Int. J. Adv. Manuf. Technol. 43, 276–286 (2009)

    Article  Google Scholar 

  9. 9.

    Hashemi, M.: Modeling of the rotary-screw-driven dispensing process. http://hdl.handle.net/10388/etd-04172006-160911 (2006). Accessed 12 July 2019

  10. 10.

    Doyle, D.G.; Forget, R.J.; Prentice, T.C.; Mattero, P.A.: Method and apparatus for dispensing a viscous material on a substrate. United States patent US 9,636,699 (2017)

  11. 11.

    Chen, X.B.; Kai, J.: Modeling of positive-displacement fluid dispensing processes. IEEE Trans. Electron. Packag. Manuf. 27, 157–163 (2004)

    Article  Google Scholar 

  12. 12.

    Chen, X.B.; Shoenau, G.; Zhang, W.J.: Modeling of time-pressure fluid dispensing processes. IEEE Trans. Electron. Packag. Manuf. 23, 300–305 (2000)

    Article  Google Scholar 

  13. 13.

    Dixon, D.; Kazalski, J.; Murch, F.; Marongelli, S.: Practical issues concerning dispensing pump technologies-adhesive bonds to a surface in a way that is directly proportional to its area. Circuits Assem. 8, 36–41 (1997)

    Google Scholar 

  14. 14.

    Abas, M.; Salman, Q.; Khan, A.M.; Rahman, K.: Direct ink writing of flexible electronic circuits and their characterization. J. Braz. Soc. Mech. Sci. Eng. (2019). https://doi.org/10.1007/s40430-019-2066-3

    Article  Google Scholar 

  15. 15.

    Cao, Y.; Zhou, L.; Wang, X.; Li, X.; Zeng, X.: MicroPen direct-write deposition of polyimide. Microelectron. Eng. 86, 1989–1993 (2009)

    Article  Google Scholar 

  16. 16.

    Wang, Z.; Cao, Y.; Li, X.; Gao, M.; Zeng, X.: Fabrication of fluorinated polyimide optical waveguides by micropen direct writing technology. Opt. Lasers Eng. 49, 880–884 (2011)

    Article  Google Scholar 

  17. 17.

    Vlasea, M.; Toyserkani, E.: Experimental characterization and numerical modeling of a micro-syringe deposition system for dispensing sacrificial photopolymers on particulate ceramic substrates. J. Mater. Process. Technol. 213, 1970–1977 (2013)

    Article  Google Scholar 

  18. 18.

    Sun, J.; Ng, J.H.; Fuh, Y.H.; San Wong, Y.; Loh, H.T.; Xu, Q.: Comparison of micro-dispensing performance between micro-valve and piezoelectric printhead. Microsyst. Technol. 15, 1437–1448 (2009)

    Article  Google Scholar 

  19. 19.

    Jiang, Z.-L.; Liu, Y.-Y.; Chen, H.-P.; Zhang, Y.-N.; Hu, Q.-X.: Multi-objective optimization of process parameters for biological 3D printing composite forming based on SNR and grey correlation degree. Int. J. Adv. Manuf. Technol. 80, 549–554 (2015)

    Article  Google Scholar 

  20. 20.

    Bai, Y.-Q.; Kim, M.-K.; Lee, I.H.; Cho, H.Y.: Fabrication of a planar spiral antenna using direct writing technology. J. Mech. Sci. Technol. 29, 2461–2465 (2015)

    Article  Google Scholar 

  21. 21.

    Pardeshi, P.M.; Mungray, A.A.; Mungray, A.K.: Determination of optimum conditions in forward osmosis using a combined Taguchi–neural approach. Chem. Eng. Res. Des. 109, 215–225 (2016)

    Article  Google Scholar 

  22. 22.

    Qureshi, R.F.; Bhatti, I.; Qureshi, K.; Memon, S.I.: Parametric study of rupture analysis for the optimization of stable emulsion liquid membrane using DOE approach. Mehran Univ. Res. J. Eng. Technol. 39, 524–531 (2020)

    Article  Google Scholar 

  23. 23.

    Yang, C.-B.; Deng, C.-S.; Chiang, H.-L.: Combining the Taguchi method with artificial neural network to construct a prediction model of a CO2 laser cutting experiment. Int. J. Adv. Manuf. Technol. 59, 1103–1111 (2012)

    Article  Google Scholar 

  24. 24.

    Muñoz-Rubio, A.; Bienvenido-Huertas, D.; Bermúdez-Rodríguez, F.J.; Tornell-Barbosa, M.: Design optimization of the aeronautical sheet hydroforming process using the Taguchi method. Appl. Sci. 9, 1932 (2019)

    Article  Google Scholar 

  25. 25.

    Pai, P.S.; Rao, B.R.S.: Artificial neural network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Appl. Energy 88, 2344–2354 (2011)

    Article  Google Scholar 

  26. 26.

    Moosavi, V.; Vafakhah, M.; Shirmohammadi, B.; Ranjbar, M.: Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arab. J. Sci. Eng. 39, 1785–1796 (2014). https://doi.org/10.1007/s13369-013-0762-3

    Article  Google Scholar 

  27. 27.

    Reed, R.; MarksII, R.J.: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. MIT Press, Cambridge (1999)

    Google Scholar 

  28. 28.

    Prabhu, S.; Uma, M.; Vinayagam, B.K.: Surface roughness prediction using Taguchi-fuzzy logic-neural network analysis for CNT nanofluids based grinding process. Neural Comput. Appl. 26, 41–55 (2015)

    Article  Google Scholar 

  29. 29.

    Lin, H.-L.; Chou, C.-P.: Optimization of the GTA welding process using combination of the Taguchi method and a neural-genetic approach. Mater. Manuf. Process. 25, 631–636 (2010)

    Article  Google Scholar 

  30. 30.

    Zeydan, M.: Improvement of process conditions in acrylic fiber dyeing using gray-based Taguchi-neural network approach. Neural Comput. Appl. 25, 155–170 (2014)

    Article  Google Scholar 

  31. 31.

    Vishal, S.; Joshy, J.; Ratan, P.; Rathnakar, G.; Ravichandran, G.: Taguchi-based ANN predictions to analyze the tensile strength of adhesive-bonded single lap joints. Mater. Perform. Charact. 7, 186–201 (2018)

    Google Scholar 

  32. 32.

    Sanjari, M.; Taheri, A.K.; Movahedi, M.R.: An optimization method for radial forging process using ANN and Taguchi method. Int. J. Adv. Manuf. Technol. 40, 776–784 (2009)

    Article  Google Scholar 

  33. 33.

    Abdul, R.; Guo, G.; Chen, J.C.; Yoo, J.J.-W.: Shrinkage prediction of injection molded high density polyethylene parts with Taguchi/artificial neural network hybrid experimental design. Int. J. Interact. Des. Manuf. 14, 345–357 (2020)

    Article  Google Scholar 

  34. 34.

    Shakeel, M.; Khan, W.A.; Rahman, K.: Fabrication of cost effective and high sensitivity resistive strain gauge using DIW technique. Sens. Actuators A Phys. 258, 123–130 (2017)

    Article  Google Scholar 

  35. 35.

    Usman Jan, Q.M.; Habib, T.; Noor, S.; Abas, M.; Azim, S.; Yaseen, Q.M.: Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage’s using integrated approach of S/N ratio and composite desirability function. Cogent Eng. (2020). https://doi.org/10.1080/23311916.2020.1781424

    Article  Google Scholar 

  36. 36.

    Abas, M.; Sayd, L.; Akhtar, R.; Khalid, Q.S.; Khan, A.M.; Pruncu, C.I.: Optimization of machining parameters of aluminum alloy 6026-T9 under MQL-assisted turning process. J. Mater. Res. Technol. 9, 10916–10940 (2020). https://doi.org/10.1016/j.jmrt.2020.07.071

    Article  Google Scholar 

  37. 37.

    Arab Chamjangali, M.: Modelling of cytotoxicity data (CC50) of anti-HIV 1-[5-chlorophenyl) sulfonyl]-1H-pyrrole derivatives using calculated molecular descriptors and Levenberg–Marquardt artificial neural network. Chem. Biol. Drug Des. 73, 456–465 (2009)

    Article  Google Scholar 

  38. 38.

    Oymak, S.; Soltanolkotabi, M.: Overparameterized nonlinear learning: Gradient descent takes the shortest path? In: International Conference on Machine Learning, pp. 4951–4960 (2019)

  39. 39.

    Žic, M.; Subotić, V.; Pereverzyev, S.; Fajfar, I.: Solving CNLS problems using Levenberg–Marquardt algorithm: a new fitting strategy combining limits and a symbolic Jacobian matrix. J. Electroanal. Chem. 866, 114171 (2020)

    Article  Google Scholar 

  40. 40.

    Luo, X.J.; Oyedele, L.O.; Ajayi, A.O.; Akinade, O.O.; Delgado, J.M.D.; Owolabi, H.A.; Ahmed, A.: Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings. Energy AI. 2, 100015 (2020)

    Article  Google Scholar 

  41. 41.

    Zupan, J.; Gasteiger, J.: Neural Networks for Chemists: An Introduction. John Wiley & Sons, Inc., Hoboken (1993)

    Google Scholar 

  42. 42.

    Vehtari, A.; Gelman, A.; Gabry, J.: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017)

    MathSciNet  Article  Google Scholar 

  43. 43.

    Yun, H.; Kim, H.; Lee, I.: Research for improved flexible tactile sensor sensitivity. J. Mech. Sci. Technol. 29, 5133–5138 (2015)

    Article  Google Scholar 

  44. 44.

    Jin, Y.; Zhao, D.; Huang, Y.: Study of extrudability and standoff distance effect during nanoclay-enabled direct printing. Bio-Des. Manuf. 1, 123–134 (2018)

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Khalid Rahman.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abas, M., Naeem, K., Habib, T. et al. Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network. Arab J Sci Eng 46, 2429–2442 (2021). https://doi.org/10.1007/s13369-020-05103-3

Download citation

Keywords

  • Conductive pattern lines
  • Prediction model
  • Signal-to-noise ratios
  • Microdispensing system
  • Artificial neural networks