Artificial neural network-based predictive model for output characteristics in drilling of quartz cyanate ester polymeric composite

Document Type : Article

Authors

1 Advanced Systems Laboratory, DRDO, Hyderabad, Telangana, India, 500058

2 Department of Mechanical Engineering, National Institute of Technology, Warangal, Telangana, India, 506004

Abstract

Apart from the widely used polymeric fibers, Quartz fiber is the one which possess various characteristics. Quartz polymeric fiber in combination with Cyanate Ester resin produces high-performance composite which has excellent properties and used primarily in military applications. The present investigation aims at developing a model to predict the output characteristics of hole in the drilling of Quartz composite laminate. Output parameters considered are thrust force, torque, exit delamination factor, hole diameter, cylindricity and surface roughness. Vacuum Assisted Resin Transfer Moulding (VARTM) process was adopted for the manufacturing of the laminate. Full factorial design of experiments was considered for the selected input parameters and experiments were carried out. Further model was developed to predict the output parameters employing Back Propagation Neural Network (BPNN) method and found that the optimal network architecture is 3-45-15-10-6 with Mean Squared Error (MSE) of 0.0105 . Experimental results were analyzed and studied the influence of input parameters in this drilling process. The testing data show a good match with the output parameters predicted from the model and maximum error obtained is 7.58%. Further, the model developed was validated with new batch of experiments and the values obtained are satisfactory with maximum error of 7.17%.

Keywords

References

References:
1. Donald, V.R. and Dominick, V.R., Reinforced Plastics Handbook, 3rd Edn., Elsevier (2005).
2. Nafiz, Y. and Mustafa, G. "The influences of varying feed rate on hole quality and force in drilling CFRP composite", J of Science, 30, pp. 39-50 (2017).
3. Sorrentino, L., Turchetta, S., and Bellini, C. "A new method to reduce delaminations during drilling of FRP laminates by feed rate control", Compos. Struct., 186, pp. 154-164 (2018).
4. Won, M.S. and Dharan, C.K.H. "Drilling of aramid and carbon fiber polymer composites", J of Manuf. Sci. and Eng., 124, pp. 778-783 (2002).
5. Halil, B.K., Ali, U., Murat, K., et al. "A novelty optimization approach for drilling of CFRP nanocomposite laminates", Int. J Adv. Manuf. Technol., 100(9-12), pp. 2995-3012 (2019).
6. Chander, P., Alokesh, P., Animesh, K.B., et al. "Investigating the efficacy of adhesive tape for drilling carbon fibre reinforced polymers", Materials, 14, pp. 1699-1716 (2021).
7. John, K.M. and Kumaran, S.T. "Backup support technique towards damage-free drilling of composite materials: A review", Int. J Lightweight Mater. Manuf., 3, pp. 357-364 (2020).
8. Davim, J.P. and Reis, P. "Study of delamination in drilling carbon fiber reinforced plastics (CFRP) using design experiments", Compos. Struct., 59(4), pp. 481-487 (2003).
9. Kishan, Z., Din, B., Soni, K., et al. "Recent trends in drilling of carbon fiber reinforced polymers (CFRPs): A state-of the art review", J Manuf. Process., 69, pp. 47-68 (2021).
10. Tsao, C.C. and Hocheng, H. "Taguchi analysis of delamination associated with various drill bits in drilling of composite material", Int. J of Mach. Tools and Manuf., 44(10), pp. 1085-1090 (2004).
11. Vijayan, K., Prabukarthi, A., Arun, R., et al. "Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates", Compos. B. Eng., 43, pp. 1791-1799 (2012).
12. Anarghya, A., Harshith, D.N., Nitish, R., et al. "Thrust and torque force analysis in the drilling of aramid fibre-reinforced composite laminates using RSM and MLPNN-GA", Heliyon, 4, p. 39 (2018).
13. Lee, J.H., Ge, J.C., and Song, J.H. "Study on burr formation and tool wear in drilling CFRP and its hybrid composites", Appl. Sci., 11(1), p. 384 (2021).
14. Kalita, K., Mallick, P.K., Bhoi, A.K., et al. "Optimizing drilling induced delamination in GFRP composites using genetic algorithm & particle swarm optimization", Adv. Compos. Lett., 27, pp. 1-9 (2018).
15. Ali, U., Murat, K., and Mehmet, B. "Optimization and effects of machining parameters on delamination in drilling of pure and Al2O3/SiO2 added GFRP composites", Int. J Adv. Manuf. Technol., 119, pp. 657-675 (2022).
16. Ahmet, Y., Erol, K., Hisman, Y., et al. "Effects of cutting parameters and point angle on thrust force and delamination in drilling of CFRP", Mater. Test., 56, pp. 1046-1048 (2014).
17. Ramesh, B., Elayaperumal, A., Satishkumar, S., et al. "Effect of drill point geometry on quality characteristics and multiple performance optimization in drilling of nonlaminated composites", Proc. Inst. Mech. Eng. L., 230(2) pp. 558-568 (2016).
18. Karnik, S.R., Gaitonde, V.N., Rubio, J.C., et al. "Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model", Mater. Des., 29(9), pp. 1768-1776 (2008).
19. Himmel, C., and May, P. "Advantages of plasma etch modeling using neural networks over statistical techniques", IEEE Trans. Semicond. Manuf., 6, pp. 103-111 (1993).
20. Yang, B., Fu, K., Lee, J., et al. "Artificial Neural Network (ANN) based residual strength prediction of Carbon Fibre Reinforced Composites (CFRC) after impact", Appl. Compos. Mater., 28, pp. 809-833 (2021).
21. Guoqiang, Z., Shabshan, H., and Hongqun, T. "Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources", Adv. Compos. Lett., 30, p. 15 (2021).
22. Qian, W. and Xiaoliang, J. "Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy Cmeans", Compos. Struct., 235, 111803 (2020).
23. Soepangkat, B., Norcahyo, R., 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, pp. 593-607 (2019).
24. Fajar, P.N., Soepangkat, B., Norcahyo, R., et al. "Multi response prediction of end milling CFRP with backpropagation neural network", AIP Conf. Proc., 2114 (2019).
25. Vineela, M.G., Dave, A., and Chaganti, P.K. "Artificial neural network based prediction of tensile strength of hybrid composites", Mater. Today, 5, pp. 19908- 19915 (2018).
26. Mishra, R., Malik, J., Singh, I., et al. "Neural network approach for estimating the residual tensile strength after drilling in uni-directional glass fiber reinforced plastic laminates", Mater. Des., 31, pp. 2790-2795 (2010).
27. Soepangkat, B., Norcahyo, R., Effendi, M.K., et al. "Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO)", Eng. Sci. and Technol., 23, pp. 700- 713 (2020).
28. Gaitonde, V.N., Karnik, S.R., and Davim, J. "Taguchi multiple-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept", J of Mater. Process. Techno., 196, pp. 73-78 (2008).
29. Kuo, R. and Cohen, P. "Intelligent tool wear estimation system through artificial neural networks and fuzzy modelling", Artif. Intell. Eng., 12, pp. 229-242 (1998).
30. Mondal, N., Mandal, S., and Mandal, M.C. "FPA based optimization of drilling burr using regression analysis and ANN model", Measurement, 152, 107327 (2020).
31. Zhang, C., Minda, Y., Chen, W., et al., Gradient Descent Optimization in Deep Learning Model Training Based on Multistage and Method Combination Strategy, Hindawi (2021).
32. Abhishek, K., Datta, S., and Mahapatra, S.S. "Optimization of thrust, torque, entry and exist delamination factor during drilling of CFRP composites", Inter. J of Advan. Manuf. Techno., 76, pp. 401-416 (2014).
33. Velayudham, A., Krishnamurthy, R., and Soundarapandian, T. "Evaluation of drilling characteristics of high volume fraction fiber glass reinforced polymeric composite", Inter. J of Mach. Tools and Manuf., 45, pp. 399-406 (2005).
34. Arshinov, V. and Alekseev, G., Metal Cutting Theory and Cutting Tool Design, MIR publishers, Moscow (1976).
35. Abhishek, K., Datta, S., and Mahapatra, S.S. "Multiobjective optimization in drilling of CFRP (polyester) composites: Application of a fuzzy embedded harmony search (HS) algorithm", Measurement, 77, pp. 222-239 (2016).
36. Yasar, N., Korkmaz, M.E., and Gunay, M. "Investigationon hole quality of cutting conditions in drilling of FRP composite", MATEC Web Conf., 112 (2017).
Scientia Iranica
Volume 30, Issue 2
Transactions on Mechanical Engineering (B)
March and April 2023
Pages 391-408

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