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Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

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Abstract

An integrated approach has been applied to predict and optimize multi-performance characteristics, such as optimum thrust force (Fz), torque (Mz), hole surface roughness (Ra), delamination (D) and hole roundness (R), in drilling process of Kevlar fiber reinforced polymer. The experiments were performed by varying drill point geometry and drilling process parameters, i.e., drill point angle, feed rate, and spindle speed. The quality characteristics Fz, Mz, Ra, D, and R were the smaller the better. Taguchi orthogonal array (OA) L18 was used as the design of experiments. Grey fuzzy analysis was first applied to obtain a rough estimation of the optimum drill point geometry and drilling process parameters. Backpropagation neural network (BPNN) model was developed and utilized to predict the optimum Fz, Mz, Ra, D, and R. Genetic algorithm (GA) was performed to search for global optimum of drilling process parameters combinations. The analysis of the effect of drill point angle, as well as drilling process parameters, on the individual performance characteristics was conducted by examining both the percentage contribution of drill point geometry and drilling process parameters on the total variance of three responses individually, and the response graphs. The results of the confirmation experiment showed that the BPNN based GA optimization method could accurately predict and also significantly improve the multiple performance characteristics.

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Abbreviations

\( y_{i} \) :

Measured characteristic value of the response

\( X_{i}^{*} \left( k \right) \) :

Normalization value of the \( k \) response

\( \hbox{min} \;X_{i} \left( k \right) \) :

The smallest value of \( X_{i} \left( k \right) \) for the kth response

\( {\text{max}}\;X_{i} \left( k \right) \) :

The largest value of \( X_{i} \left( k \right) \) for the kth response

\( \zeta \) :

Distinguishing coefficient

\( \xi_{i} \left( k \right) \) :

GRG value of the kth response

\( \Delta_{0,i} \left( k \right) \) :

Deviation sequence of reference for the kth response

\( \Delta_{min} \) :

Smallest value of \( \Delta_{0,i} \)

\( \Delta_{max} \) :

Largest value of \( \Delta_{0,i} \)

References

  1. Zheng, L., Zhou, H., & Gao, C. (2012). Hole drilling in ceramics Kevlar fiber reinforced plastics double-plate composite armor using diamond core drill. Journal Material and Design, 40, 461–466.

    Article  Google Scholar 

  2. Bhattacharyya, D., & Horrigan, D. P. W. (1998). A study of hole drilling in kevlar composites. Composites Science and Technology, 58, 267–283.

    Article  Google Scholar 

  3. Krishnamoorthy, A., Boopathy, S. R., Palanikumar, K., & Davim, J. P. (2012). Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement, 45(5), 1286–1296.

    Article  Google Scholar 

  4. Shunmugesh, K., & Panneerselvam, K. (2016). Machinability study of carbon fiber reinforced polymer in the longitudinal and transverse direction and optimization of process parameters using PSO-GSA. Engineering Science and Technology an International Journal, 19, 1552–1563.

    Article  Google Scholar 

  5. Palanikumar, K., Latha, B., & Davim, J. P. (2012). Application of Taguchi method with grey fuzzy logic for the optimization of machining parameters in machining composites. Computational Methods for Optimizing Manufacturing Technology: Models and Techniques. https://doi.org/10.4018/978-1-4666-0128-4.ch009.

    Google Scholar 

  6. Rajmohan, T., Palanikumar, K., & Prakash, S. (2013). Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites. Composites Part B Engineering, 50, 297–308.

    Article  Google Scholar 

  7. Pandey, R. K., & Panda, S. S. (2014). Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement, 47, 386–392.

    Article  Google Scholar 

  8. Sakthivel, M., Vijayakumar, S., & Jenarthanan, M. P. (2017). Grey-fuzzy logic to optimise process parameters in drilling of glass fibre reinforced stainless steel mesh polymer composite. Pigment & Resin Technology, 46, 276–285.

    Article  Google Scholar 

  9. Jayabal, S., & Natarajan, U. (2010). Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using Nelder-Mead and genetic algorithm methods. International Journal Advance Manufacturing Technology, 51, 371–381.

    Article  Google Scholar 

  10. Saravanan, M., Ramalingam, D., Manikandan, G., & Kaarthikeyen, R. R. (2012). Multi objective optimization of drilling parameters using genetic algorithm. Procedia Engineering, 38, 197–207.

    Article  Google Scholar 

  11. Kannan, T. D. B., Rajeshkannan, G., Kumar, B. S., & Baskar, N. (2014). Application of artificial neural network modeling for machining parameters optimization in drilling operation. Procedia Materials Science, 5, 2242–2249.

    Article  Google Scholar 

  12. Wan, X., Wang, Y., & Zhao, D. (2016). Grey relational and neural network approach for multi-objective optimization in small scale resistance spot welding of titanium alloy. Journal of Mechanical Science and Technology, 30(6), 2675–2682.

    Article  Google Scholar 

  13. Taguchi, G. (1990). Introduction to quality engineering. Tokyo: Asian Productivity Organization.

    Google Scholar 

  14. Lin, J. L., & Lin, C. L. (2002). The use of orthogonal array with grey relational analysis to optimize the electrical discharge machining process performance with multiple characteristics. International Journal of Machine Tools and Manufacture, 42, 237–244.

    Article  Google Scholar 

  15. Soepangkat, B. O. P., Soesanti, A., & Pramujati, B. (2013). The use of Taguchi-grey-fuzzy to optimize performance characteristics in turning of AISI D2. Applied Mechanics and Materials, 315, 211–215.

    Article  Google Scholar 

  16. Ross, P. J. (2008). Taguchi technique for quality engineering. New York City: McGraw-Hill Education.

    Google Scholar 

  17. Vankanti, V. K., & Ganta, V. (2014). Optimization of process parameters in drilling of GFRP Composite using Taguchi method. Journal of Materials Research and Technology, 3(1), 35–41.

    Article  Google Scholar 

  18. Palanikumar, K. (2011). Experimental investigation and optimisation in drilling of GFRP composite. Measurement, 44, 2138–2148.

    Article  Google Scholar 

  19. Fausett, L. V. (1994). Fundamentals of neural networks: architectures, algorithm, and applications. Upper Saddle River: Prentice-Hall.

    MATH  Google Scholar 

  20. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation (pp. 318–362). Cambridge: MIT Press.

    Google Scholar 

  21. Gowda, C. C., & Mayya, S. G. (2014). Comparison of back propagation neural network and genetic algorithm neural network for stream flow prediction. Journal of Computational Environmental Sciences, 1, 1–6.

    Article  Google Scholar 

  22. Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: University of Michigan Press.

    MATH  Google Scholar 

  23. Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley Longman Publishing Co.

    MATH  Google Scholar 

  24. Gaitonde, V. N., Karnik, S. R., & Davim, J. P. (2007). Taguchi multiple-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept. Journal of Materials Processing Technology, 196, 73–78.

    Article  Google Scholar 

  25. Zhang, Z., Ming, W., Huang, H., Chen, Z., Xu, Z., Huang, Y., et al. (2015). Optimization of process parameters on surface integrity in wire electrical discharge machining of tungsten tool YG15. International Journal of Advanced Manufacturing Technology, 81, 1303–1317.

    Article  Google Scholar 

  26. Kumar, K. V., & Sait, A. N. (2017). Modelling and optimisation of machining parameters for composite pipes using artificial neural network and genetic algorithm. International Journal on Interactive Design and Manufacturing, 11(2), 435–443.

    Article  Google Scholar 

  27. Krishnaraj, V., Zitoune, R., & Davim, J. P. (2013). Drilling of polymer-matrix composites. Berlin: Springer.

    Book  Google Scholar 

  28. Kilickap, E. (2010). Optimization of cutting parameters on delamination based on Taguchi method during drilling of GFRP composite. Expert Systems with Applications, 37, 6116–6122.

    Article  Google Scholar 

  29. Armarego, E. J. A. (1996). Material removal process-twist drills and drilling operations. Parkville: University of Melbourne.

    Google Scholar 

  30. Lin, S. C. (1996). Drilling carbon fiber-reinforced composite material at high speed. Wear, 194(1), 156–162.

    Google Scholar 

  31. Tsao, C. C., & Hocheng, H. (2004). Taguchi analysis of delamination associated with various drill bits in drilling of composite material. International Journal of Machine Tools and Manufacture, 44, 1085–1090.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the PNBP Grant Provided by Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

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Authors and Affiliations

  1. Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia

    Bobby O. P. Soepangkat, Bambang Pramujati, Mohammad Khoirul Effendi & Rachmadi Norcahyo

  2. Mechanical Engineering Department, Universitas Nusantara PGRI, Kediri, Indonesia

    A. M. Mufarrih

Authors
  1. Bobby O. P. Soepangkat
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  2. Bambang Pramujati
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Correspondence to Bobby O. P. Soepangkat.

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Soepangkat, B.O.P., Pramujati, B., Effendi, M.K. et al. Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches. Int. J. Precis. Eng. Manuf. 20, 593–607 (2019). https://doi.org/10.1007/s12541-019-00017-z

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