Swarm intelligence integrated approach for experimental investigation in milling of multiwall carbon nanotube/polymer nanocomposites

• Autorzy: Kharwar Prakhar Kumar , Verma Rajesh Kumar , Mandal Nirmal Kumar , Mondal Arpan Kumar

Identyfikatory

DOI

10.24425/ame.2020.131698

• Języki publikacji: EN

Abstrakty

EN

In manufacturing industries, the selection of machine parameters is a very complicated task in a time-bound manner. The process parameters play a primary role in confirming the quality, low cost of manufacturing, high productivity, and provide the source for sustainable machining. This paper explores the milling behavior of MWCNT/epoxy nanocomposites to attain the parametric conditions having lower surface roughness (Ra) and higher materials removal rate (MRR). Milling is considered as an indispensable process employed to acquire highly accurate and precise slots. Particle swarm optimization (PSO) is very trendy among the nature-stimulated metaheuristic method used for the optimization of varying constraints. This article uses the non-dominated PSO algorithm to optimize the milling parameters, namely, MWCNT weight% (Wt.), spindle speed (N), feed rate (F), and depth of cut (D). The first setting confirmatory test demonstrates the value of Ra and MRR that are found as 1.62 µm and 5.69 mm³/min, respectively and for the second set, the obtained values of Ra and MRR are 3.74 µm and 22.83 mm³/min respectively. The Pareto set allows the manufacturer to determine the optimal setting depending on their application need. The outcomes of the proposed algorithm offer new criteria to control the milling parameters for high efficiency.

Słowa kluczowe

EN

nanocomposites; epoxy; particle; swarm; Pareto front

PL

nanokompozyty; żywica epoksydowa; cząstka; rój; Pareto front

• Wydawca: Komitet Budowy Maszyn PAN
• Czasopismo: Archive of Mechanical Engineering
• Rocznik: 2020
• Tom: LXVII, nr 3
• Strony: 353--376
• Opis fizyczny: Bibliogr. 77 poz., rys., tab., wykr.

Twórcy

autor

Kharwar Prakhar Kumar

• Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology Gorakhpur, India, 273010

autor

Verma Rajesh Kumar

• Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology Gorakhpur, India, 273010

autor

Mandal Nirmal Kumar

• Department of Mechanical Engineering, National Institute of Technical Teachers’ Training and Research, Kolkata, India,700106

autor

Mondal Arpan Kumar

• Department of Mechanical Engineering, National Institute of Technical Teachers’ Training and Research, Kolkata, India,700106

Bibliografia

• [1] M. Liu, H. Younes, H. Hong, and G.P. Peterson. Polymer nanocomposites with improved mechanical and thermal properties by magnetically aligned carbon nanotubes. Polymer, 166:81-87, 2019. doi: 10.1016/j.polymer.2019.01.031.
• [2] S.K. Singh and V.K. Verma. Exact solution of flow in a composite porous channel. Archive of Mechanical Engineering, 67(1):97-110, 2020, doi: 10.24425/ame.2020.131685.
• [3] N. Pundhir, S. Zafar, and H. Pathak. Performance evaluation of HDPE/MWCNT and HDPE/kenaf composites. Journal of Thermoplastic Composite Materials, 2019. doi: 10.1177/0892705719868278.
• [4] N. Muralidhar, V. Kaliveeran, V. Arumugam, and I.S. Reddy. Dynamic mechanical characterization of epoxy composite reinforced with areca nut husk fiber. Archive of Mechanical Engineering, 67(1):57-72, 2020, doi: 10.24425/ame.2020.131683.
• [5] F. Mostaani, M.R. Moghbeli, and H. Karimian. Electrical conductivity, aging behavior, and electromagnetic interference (EMI) shielding properties of polyaniline/MWCNT nanocomposites. Journal of Thermoplastic Composite Materials, 31(10):1393-1415, 2018. doi: 10.1177/0892705717738294.
• [6] M.R. Sanjay, P. Madhu, M. Jawaid, P. Senthamaraikannan, S. Senthil, and S. Pradeep. Characterization and properties of natural fiber polymer composites: A comprehensive review. Journal of Cleaner Production, 172:566-581, 2018. doi: 10.1016/j.jclepro.2017.10.101.
• [7] A.J. Valdani and A. Adamian. Finite element-finite volume simulation of underwater explosion and its impact on a reinforced steel plate. Archive of Mechanical Engineering, 67(1):5-30, 2020, doi: 10.24425/ame.2020.131681.
• [8] A. Kausar, I. Rafique, and B. Muhammad. Review of applications of polymer/carbon nanotubes and epoxy/CNT composites. Polymer-Plastics Technology and Engineering, 55(11):1167-1191, 2016. doi: 10.1080/03602559.2016.1163588.
• [9] E. Vajaiac, et al. Mechanical properties of multiwall carbon nanotube-epoxy composites. Digest Journal of Nanomaterials and Biostructures, 10(2):359-369, 2015.
• [10] A.E Douba, M. Emiroglu, R.A Tarefder, U.F Kandil, and M.R. Taha. Use of carbon nanotubes to improve fracture toughness of polymer concrete. Journal of the Transportation Research Board, 2612(1):96-103, 2017. doi: 10.3141/2612-11.
• [11] W. Khan, R. Sharma, and P. Saini. Carbon nanotube-based polymer composites: synthesis, properties and applications. In M.R. Berber and I.H. Hafez (eds.). Carbon Nanotubes. Current Progress and their Polymer Composites. chapter 1, pages 1-46. IntechOpen, Rijeka, Croatia, 2016. doi: 10.5772/62497.
• [12] W.M. da Silva, H. Ribeiro, J.C. Neves, A.R. Sousa, and G.G. Silva. Improved impact strength of epoxy by the addition of functionalized multiwalled carbon nanotubes and reactive diluent. Journal of Applied Polymer Science, 132(39):1-12, 2015, doi: 10.1002/app.42587.
• [13] S. Dixit, A. Mahata, D.R. Mahapatra, S.V. Kailas, and K. Chattopadhyay. Multi-layer graphene reinforced aluminum – Manufacturing of high strength composite by friction stir alloying. Composites Part B: Engineering,136: 63-71, 2018. doi: 10.1016/j.compositesb.2017.10.028.
• [14] C. Kostagiannakopoulou, X. Tsilimigkra, G. Sotiriadis, and V. Kostopoulos. Synergy effect of carbon nano-fillers on the fracture toughness of structural composites. Composites Part B: Engineering, 129:18–25, 2017. doi: 10.1016/j.compositesb.2017.07.012.
• [15] G. Romhány and G. Szebényi. Preparation of MWCNT reinforced epoxy nanocomposite and examination of its mechanical properties. Plastics, Rubber and Composites, 37(5-6):214-218, 2008. doi: 10.1179/174328908X309376.
• [16] G. Mittal, V. Dhand, K.Y. Rhee, S.J. Park, and W.R. Lee. A review on carbon nanotubes and graphene as fillers in reinforced polymer nanocomposites. Journal of Industrial and Engineering Chemistry, 21:11-25, 2015. doi: 10.1016/j.jiec.2014.03.022.
• [17] S. H. Behzad, M.J. Kimya, G. Mehrnaz. Mechanical properties of multi-walled carbon nanotube/epoxy polysulfide nanocomposite. Journal of Materials & Design, 50:62-67, 2013.
• [18] N. Yu, Z.H. Zhang, and S.Y. He. Fracture toughness and fatigue life of MWCNT/epoxy composites. Materials Science and Engineering: A, 494(1-2):380:384, 2018. doi: 10.1016/j.msea.2008.04.051.
• [19] J.G. Park, et al. Thermal conductivity of MWCNT/epoxy composites: The effects of length, alignment and functionalization. Carbon, 50(6):2083-2090, 2012. doi: 10.1016/j.carbon.2011.12.046.
• [20] B. Singaravel and T. Selvaraj. Optimization of machining parameters in turning operation using combined TOPSIS and AHP method. Tehnički Vjesnik, 22(6):1475-1480, 2015. doi: 10.17559/TV-20140530140610.
• [21] N. Kaushik and S. Singhal. Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiCp matrix composite. Production & Manufacturing Research, 6(1):171-189, 2018. doi: 10.1080/21693277.2018.1479666.
• [22] S.O.N. Raj and S. Prabhu. Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode. International Journal of Machining and Machinability of Materials, 19(1):76-94, 2017. doi: 10.1504/IJMMM.2017.081190.
• [23] S. Chakraborty. Applications of the MOORA method for decision making in manufacturing environment. International Journal of Advanced Manufacturing Technology, 54(9-12):1155-1166, 2011. doi: 10.1007/s00170-010-2972-0.
• [24] M.P. Jenarthanan and R. Jeyapaul. Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology, 5(4):23-36, 2013. doi: 10.4314/ijest.v5i4.3.
• [25] T.V. Sibalija. Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008-2018). Applied Soft Computing, 84:105743, ISSN 1568-4946, doi: 10.1016/j.asoc.2019.105743.
• [26] J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of the ICNN’95 – International Conference on Neural Networks, pages 1942-1948, Perth, Australia, 27 Nov.-1 Dec. 1995. doi: 10.1109/ICNN.1995.488968.
• [27] F. Cus and J. Balic. Optimization of cutting process by GA approach. Robotics and Computer-Integrated Manufacturing, 19(1-2):113-121, 2003. doi: 10.1016/S0736-5845(02)00068-6.
• [28] M.N. Ab Wahab, S. Nefti-Meziani, and A. Atyabi. A comprehensive review of swarm optimization algorithms. PLoS One, 10(5): e0122827, 2015. doi: 10.1371/journal.pone.0122827.
• [29] A. Del Prete, R. Franchi, and D. De Lorenzis. Optimization of turning process through the analytic flank wear modelling. AIP Conference Proceedings, 1960:070008, 2018.doi: 10.1063/1.5034904.
• [30] G. Xu and Z. Yang. Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis. International Journal of Advanced Manufacturing Technology, 78(1-4):525-536, 2015. doi: 10.1007/s00170-014-6643-4.
• [31] H. Juan, S.F. Yu, and B.Y. Lee. The optimal cutting parameter selection of production cost in HSM for SKD61 tool steels. International Journal of Machine Tools and Manufacturing, 43(7):679-686, 2003. doi: 10.1016/S0890-6955(03)00038-5.
• [32] U. Zuperl and F. Cus. Optimization of cutting conditions during cutting by using neural networks. Robotics and Computer-Integrated Manufacturing, 19(1-2):189-199, 2003. doi: 10.1016/S0736-5845(02)00079-0.
• [33] P.E. Amiolemhen and A.O.A. Ibhadode. Application of genetic algorithms – determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. International Journal of Machine Tools and Manufacture, 44(12-13):1403-1412, 2004. doi: 10.1016/j.ijmachtools.2004.02.001.
• [34] E.O. Ezugwu, D.A. Fadare, J. Bonney, R.B. Da Silva, and W.F. Sales. Modeling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. International Journal of Machine Tools and Manufacturing, 45(12-13):1375-1385, 2005. doi: 10.1016/j.ijmachtools.2005.02.004.
• [35] P. Asokan, N. Baskar, K. Babu, G. Prabhakaran, and R. Saravanan. Optimization of surface grinding operation using particle swarm optimization technique. Journal of Manufacturing Science and Engineering, 127(4):885-892, 2015. doi: 10.1115/1.2037085.
• [36] R.Q. Sardinas, M.R. Santana, and E.A. Brindis. Genetic algorithm-based multio-bjective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 19(2):127-133, 2006. doi: 10.1016/j.engappai.2005.06.007.
• [37] C. Jia and H. Zhu. An improved multiobjective particle swarm optimization based on culture algorithms. Algorithms, 10(2):46-56, 2017. doi: 10.3390/a10020046.
• [38] C.A. Coello Coello, G.T. Pulido, and M.S. Lechuga. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3):256-279, 2004. doi: 10.1109/TEVC.2004.826067.
• [39] C.R. Raquel and P.C. Naval. An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pages 257-264, Washington DC, USA, 2005. doi: 10.1145/1068009.1068047.
• [40] G.T. Pulido and C.A. Coello Coello. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 225-237, Seattle, USA, 2004. doi: 10.1007/978-3-540-24854-5_20.
• [41] S. Mostaghim and J. Teich. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS’03), pages 26-33, Indianapolis, IN, USA, 26 April 2003. doi: 10.1109/SIS.2003.1202243.
• [42] J. Branke and S. Mostaghim. About selecting the personal best in multi-objective particle swarm optimization. In Proceedings of the Parallel Problem Solving From Nature (PPSN Ix) International Conference, pages 523-532, Reykjavik, Iceland, 9-13 September 2006. doi: 10.1007/11844297_53.
• [43] T.M. Chenthil Jegan and D. Ravindran. Electrochemical machining process parameter optimization using particle swarm optimization. Computational Intelligence, 33:1019-1037, 2017. doi: 10.1111/coin.12139.
• [44] C.P. Mohanty, S.S. Mahapatra, and M.R. Singh. A particle swarm approach for multi-objective optimization of electrical discharge machining process. Journal of Intelligent Manufacturing, 27:1171-1190, 2016. doi: 10.1007/s10845-014-0942-3.
• [45] U. Natarajan, V.M. Periasamy, and R. Saravanan. Application of particle swarm optimisation in artificial neural network for the prediction of tool life. The International Journal of Advanced Manufacturing Technology, 31:871-876, 2007. doi: 10.1007/s00170-005-0252-1.
• [46] A.K. Gandhi, S.K. Kumar, M.K. Pandey, and M.K. Tiwari. EMPSO-based optimization for inter-temporal multi-product revenue management under salvage consideration. Applied Soft Computing, 11(1):468-476, 2011. doi: 10.1016/j.asoc.2009.12.006.
• [47] J.J. Yang, J.Z. Zhou, W. Wu, and F. Liu. Application of improved particle swarm optimization in economic dispatching. Power System Technology, 29(2):1-4, 2005.
• [48] T. Sibalija, S. Pentronic, and D. Milovanovic. Experimental optimization of nimonic 263 laser cutting using a particle swarm approach. Metals 9:1147, 2019. doi: 10.3390/met9111147.
• [49] X. Luan, H. Younse, H. Hong, G.P. Peterson. Improving mechanical properties of PVA based nano composite using aligned single-wall carbon nanotubes. Materials Research Express, 6(10):1050a6, 2019. doi: 10.1088/2053-1591/ab4058.
• [50] H. Younes, R.A. Al-Rub, M.M. Rahman, A. Dalaq, A.A. Ghaferi, and T. Shah. Processing and property investigation of high-density carbon nanostructured papers with superior conductive and mechanical properties. Diamond and Related Materials, 68:109-117, 2016. doi: 10.1016/j.diamond.2016.06.016.
• [51] G. Christensen, H. Younes, H. Hong, and G.P. Peterson. Alignment of carbon nanotubes comprising magnetically sensitive metal oxides by nonionic chemical surfactants. Journal of Nanofluids, 2(1): 25-28, 2013. doi: 10.1166/jon.2013.1031.
• [52] H. Younes, M.M. Rahman, A.A. Ghaferi, and I. Saadat. Effect of saline solution on the electrical response of single wall carbon nanotubes-epoxy nanocomposites. Journal of Nanomaterials, 2017: 6843403, 2017 doi: 10.1155/2017/6843403.
• [53] H. Younes, G. Christensen, L. Groven, H. Hong, and P. Smith. Three dimensional (3D) percolation network structure: Key to form stable carbon nano grease. Journal of Applied Research and Technology, 14(6):375-382, 2016. doi: 10.1016/j.jart.2016.09.002.
• [54] J. Jerald, P. Asokan, G. Prabaharan, and R. Saravanan. Scheduling optimization of flexible manufacturing systems using particle swarm optimization algorithm. The International Journal of Advanced Manufacturig Technology, 25:964-971, 2005. doi: 10.1007/s00170-003-1933-2.
• [55] M. Ghasemi, E. Akbari, A. Rahimnejad, S.E. Razavi, S. Ghavidel, and L. Li. Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23:9701-9718, 2019. doi: 10.1007/s00500-018-3536-8.
• [56] M.R. Singh and S.S. Mahapatra. A swarm optimization approach for flexible flow shop scheduling with multiprocessor tasks. The International Journal of Advanced Manufacturing Technology, 62(1-4), 267-277, 2012. doi: 10.1007/s00170-011-3807-3.
• [57] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems, 15(4):1232-1239, 2000. doi: 10.1109/59.898095.
• [58] F. Belmecheri, C. Prins, F. Yalaoui, and L. Amodeo. Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing, 24(4):775-789, 2013. doi: 10.1007/s10845-012-0627-8.
• [59] M. Bachlaus, M.K. Pandey, C. Mahajan, R. Shankar, and M.K. Tiwari. Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19(6):747-761, 2008. doi: 10.1007/s10845-008-0125-1.
• [60] B. Brandstatter and U. Baumgartner. Particle swarm optimization – mass-spring system analogon. IEEE Transactions on Magnetics, 38(2):997-1000, 2002. doi: 10.1109/20.996256.
• [61] B. Kim and S. Son. A probability matrix-based particle swarm optimization for the capacitated vehicle routing problem. Journal of Intelligent Manufacturing, 23(4):1119-1126, 2012. doi: 10.1007/s10845-010-0455-7.
• [62] C.H. Wu, D.Z. Wang, A. Ip, D.W. Wang, C.Y. Chan, and H.F. Wan. A particle swarm optimization approach for components placement inspection on printed circuit boards. Journal of Intelligent Manufacturing, 20(5):535-549, 2009. doi: 10.1007/s10845-008-0140-2.
• [63] S.B. Raja and N. Baskar. Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Systems with Applications, 39(5):5982-5989, 2012. doi: 10.1016/j.eswa.2011.11.110.
• [64] N. Yusup, A.M. Zain, and S.Z.M. Hashim. Overview of PSO for optimizing process parameters of machining. Procedia Engineering, 29:914-923, 2012. doi: 10.1016/j.proeng.2012.01.064.
• [65] R.L. Malghan, K.M.C. Rao, A.K. Shettigar, S.S. Rao, and R.J. D’Souza. Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(9):2541-3553, 2017. doi: 10.1007/s40430-016-0675-7.
• [66] A. Hadidi, A. Kaveh, B. Farahmand Azar, S. Talatahari, and C. Farahmandpour. An efficient optimization algorithm based on particle swarm and simulated annealing for space trusses. International Journal of Optimization in Civil Engineering, 3:377-395, 2011.
• [67] T. Chaudhary, A.N. Siddiquee, A.K. Chanda, and Z.A. Khan. On micromachining with a focus on miniature gears by non conventional processes: a status report. Archive of Mechanical Engineering, 65(1):129-169, 2018. doi: 10.24425/119413.
• [68] D. Kumar and K.K Singh. An experimental investigation of surface roughness in the drilling of MWCNT doped carbon/epoxy polymeric composite material.IOP Conference Series: Materials Science and Engineering, 149:012096, 2016. doi: 10.1088/1757-899X/149/1/012096.
• [69] Niharika, B.P. Agrawal, I.A. Khan, and Z.A. Khan. Effects of cutting parameters on quality of surface produced by machining of titanium alloy and their optimization. Archive of Mechanical Engineering, 63(4):531-548, 2016. doi: 10.1515/meceng-2016-0030.
• [70] N.S. Kumar, A. Shetty, Ashay Shetty, K. Ananth, and H. Shetty. Effect of spindle speed and feed rate on surface roughness of carbon steels in CNC turning. Procedia Engineering, 38:691-697, 2012. doi: 10.1016/j.proeng.2012.06.087.
• [71] E.T. Akinlabi, I. Mathoho, M.P. Mubiayi, C. Mbohwa, and M.E. Makhatha. Effect of process parameters on surface roughness during dry and flood milling of Ti-6A-l4V. In: 2018 IEEE 9th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), pages 144-147, Cape Town, South Africa, 10-13 February 2018. doi: 10.1109/ICMIMT.2018.8340438.
• [72] J.P. Davim, L.R. Silva, A. Festas, and A.M. Abrão. Machinability study on precision turning of PA66 polyamide with and without glass fiber reinforcing. Materials & Design, 30(2):228-234, 2009. doi: 10.1016/j.matdes.2008.05.003.
• [73] J. Cha, J. Kim, S. Ryu, and S.H. Hong. Comparison to mechanical properties of epoxy nanocomposites reinforced by functionalized carbon nanotubes and graphene nanoplatelets. Composites Part B: Engineering, 162:283-288, 2018. doi: 10.1016/j.compositesb.2018.11.011.
• [74] R.V. Rao, P.J. Pawar, and R. Shankar. Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(8):949-958, 2008. doi: 10.1243/09544054JEM1158.
• [75] R. Farshbaf Zinati, M.R. Razfar, and H. Nazockdast. Surface integrity investigation for milling PA6/MWCNT. Materials and Manufacturing Processes, 30(8):1035-1041, 2014. doi: 10.1080/10426914.2014.961473.
• [76] I. Shyha, G.Y. Fu, D.H. Huo, B. Le, F. Inam, M.S. Saharudin, and J.C. Wei. Micro-machining of nano-polymer composites reinforced with graphene and nano-clay fillers. Key Engineering Materials, 786:197-205, 2018. doi: 10.4028/www.scientific.net/kem.786.197.
• [77] G. Fu, D. Huo, I. Shyha, K. Pancholi, and M.S. Saharudin. Experimental investigation on micro milling of polyester/halloysite nano-clay nanocomposites. Nanomaterials, 9(7):917, 2019. doi: 10.3390/nano9070917.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).