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Modified whale intelligence algorithm and Combined Compromise Solution (CoCoSo) for machinability evaluation of polymer nanocomposites

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Abstract

Carbon nano-onions (CNOs) are distinct from other carbon nanomaterials due to their distinctive physical and morphological features. Their addition can substantially increase the thermal, mechanical, tribological, and electrical properties. The present work explores the Milling machinability of zero-dimensional CNO reinforced epoxy nanocomposites. The effect of varying parameters, namely, the weight percentage of CNO (wt%), spindle speed (N), feed rate (F), and depth of cut (D), was examined to control the machining performances. Milling tests were conducted using the Taguchi-based L27 Orthogonal array (OA), and a nonlinear regression model was used to develop a correlation between machining constraints and responses. A comparatively advanced metaheuristic multi-objective whale optimization algorithm (MOWOA) is used to accomplish an optimal parametric set. This optimization methodology was exploited to achieve non-dominated solutions and established the Pareto front. In the end, combined compromise solution (CoCoSo) was utilized to locate the most relevant result from the Pareto optimum setting. Based on the CoCoSo analysis, the optimal solution was found as CNO ≈ 1.5 wt%, spindle speed ≈ 1500 rpm, feed rate = 50 mm/min, and depth of cut ≈ 2 mm. The findings of the microscopy test support the results of the proposed optimization tool. The suggested MOW optimization procedure could be used in the production sector for monitoring quality and productivity indices. The proposed hybrid module can be forwarded to the industrial sector to optimize the multiple conflicting responses.

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References

  1. Esawi AMK, Farag MM (2007) Carbon nanotube reinforced composites: potential and current challenges. Mater Des 28:2394–2401. https://doi.org/10.1016/j.matdes.2006.09.022

    Article  CAS  Google Scholar 

  2. Kausar A, Rafique I, Muhammad B (2016) Review of applications of polymer/carbon nanotubes and epoxy/CNT composites. Polym Plast Technol Eng 55:1167–1191

    Article  CAS  Google Scholar 

  3. Mishra N, Das G, Ansaldo A et al (2012) Pyrolysis of waste polypropylene for the synthesis of carbon nanotubes. J Anal Appl Pyrolysis 94:91–98. https://doi.org/10.1016/j.jaap.2011.11.012

    Article  CAS  Google Scholar 

  4. Gupta S, Kim H, Kim H, Loh KJ (2021) Planar capacitive imaging for composite delamination damage characterization. Meas Sci Technol 32:024010. https://doi.org/10.1088/1361-6501/abb484

    Article  CAS  ADS  Google Scholar 

  5. Kyzas GZ, Deliyanni EA, Matis KA et al (2018) Emerging nanocomposite biomaterials as biomedical adsorbents: an overview. Compos Interfaces 25:415–454. https://doi.org/10.1080/09276440.2017.1361716

    Article  CAS  ADS  Google Scholar 

  6. Keyte J, Pancholi K, Njuguna J (2019) Recent developments in graphene oxide/epoxy carbon fiber-reinforced composites. Front Mater 6:1–30. https://doi.org/10.3389/fmats.2019.00224

    Article  Google Scholar 

  7. Pikhurov DV, Zuev VV (2014) The effect of fullerene C60 on the dielectric behaviour of epoxy resin at low nanofiller loading. Chem Phys Lett 601:13–15. https://doi.org/10.1016/j.cplett.2014.03.056

    Article  CAS  ADS  Google Scholar 

  8. Jones RM (2018) Mechanics of composite materials. CRC Press

    Book  Google Scholar 

  9. Argon AS, Cohen RE (2003) Toughenability of polymers. Polymer 44:6013–6032. https://doi.org/10.1016/S0032-3861(03)00546-9

    Article  CAS  Google Scholar 

  10. Njuguna J, Ansari F, Sachse S, Rodriguez VM, Siqqique S, Zhu H (2014) Nanomaterials, nanofillers, and nanocomposites: types and properties. In: Health and environmental safety of nanomaterials. Woodhead Publishing, Elsevier Inc, pp 3–27. https://doi.org/10.1533/9780857096678.1.3

    Chapter  Google Scholar 

  11. Njuguna J, Pielichowski K (2003) Polymer nanocomposites for aerospace applications: properties. Adv Eng Mater 5:769–778

    Article  CAS  Google Scholar 

  12. Chapman R, Mulvaney P (2001) Electro-optical shifts in silver nanoparticle films. Chem Phys Lett 349:358–362. https://doi.org/10.1016/S0009-2614(01)01145-9

    Article  CAS  ADS  Google Scholar 

  13. Leszczyńska A, Njuguna J, Pielichowski K, Banerjee JR (2007) Polymer/montmorillonite nanocomposites with improved thermal properties. Thermochim Acta 454:1–22. https://doi.org/10.1016/j.tca.2006.11.003

    Article  CAS  Google Scholar 

  14. Schmidt G, Malwitz MM (2003) Properties of polymer-nanoparticle composites. Curr Opin Colloid Interface Sci 8:103–108

    Article  CAS  Google Scholar 

  15. Lau KT, Gu C, Hui D (2006) A critical review on nanotube and nanotube/nanoclay related polymer composite materials. Compos Part B Eng 37:425–436. https://doi.org/10.1016/j.compositesb.2006.02.020

    Article  CAS  Google Scholar 

  16. Seyhan AT, Tanoǧlu M, Schulte K (2009) Tensile mechanical behavior and fracture toughness of MWCNT and DWCNT modified vinyl-ester/polyester hybrid nanocomposites produced by 3-roll milling. Mater Sci Eng A 523:85–92. https://doi.org/10.1016/j.msea.2009.05.035

    Article  CAS  Google Scholar 

  17. Yang CK, Lee YR, Hsieh TH et al (2018) Mechanical property of multiwall carbon nanotube reinforced polymer composites. Polym Polym Compos 26:99–104. https://doi.org/10.1177/096739111802600112

    Article  CAS  Google Scholar 

  18. Al-rawi KR, Hedar A, Mahmood OA (2014) Effect different multi-walled carbon nanotubes MWCNTs type on mechanical properties of epoxy resin nanocomposites. Int J Appl Innov Eng Manag 3:132–137

    Google Scholar 

  19. Tarfaoui M, Lafdi K, El Moumen A (2016) Mechanical properties of carbon nanotubes based polymer composites. Compos Part B Eng 103:113–121. https://doi.org/10.1016/j.compositesb.2016.08.016

    Article  CAS  Google Scholar 

  20. Nguyen H, Zatar W, Mutsuyoshi H (2017) Hybrid polymer composites for structural applications. In: Hybrid polymer composite materials: applications. Woodhead Publishing, Elsevier Inc, pp 35–51. https://doi.org/10.1016/B978-0-08-100785-3.00002-4

    Chapter  Google Scholar 

  21. Dahiya R, Manohar B, Kapdi R (2016) Analysis of mechanical properties of carbon nanotube reinforced polymer composite for aircraft wings. Int J Res Mech Eng Technol 5762:100–104

    Google Scholar 

  22. Singh DK, Verma RK (2023) Development of reduced graphene oxide modified ultrahigh molecular weight polyethylene (rGO/UHMWPE) based nanocomposites for biomedical applications. J Thermoplast Compos Mater 36:3516–3551. https://doi.org/10.1177/08927057221129486

    Article  CAS  Google Scholar 

  23. Balaji K, Siva Kumar M, Yuvaraj N (2021) Multi objective Taguchi–Grey relational analysis and krill herd algorithm approaches to investigate the parametric optimization in abrasive water jet drilling of stainless steel. Appl Soft Comput 102:107075. https://doi.org/10.1016/J.ASOC.2020.107075

    Article  Google Scholar 

  24. Hegab H, Salem A, Rahnamayan S, Kishawy HA (2021) Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant. Appl Soft Comput 108:107416. https://doi.org/10.1016/J.ASOC.2021.107416

    Article  Google Scholar 

  25. Rajeswari B, Amirthagadeswaran KS (2018) Study of machinability and parametric optimization of end milling on aluminium hybrid composites using multi-objective genetic algorithm. J Braz Soc Mech Sci Eng 40:377. https://doi.org/10.1007/s40430-018-1293-3

    Article  CAS  Google Scholar 

  26. Yue HT, Guo CG, Li Q et al (2020) Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm. J Braz Soc Mech Sci Eng 42:1–13. https://doi.org/10.1007/s40430-020-02514-z

    Article  Google Scholar 

  27. Cepero-Mejías F, Curiel-Sosa JL, Blázquez A et al (2020) Review of recent developments and induced damage assessment in the modelling of the machining of long fibre reinforced polymer composites. Compos Struct 240:112006. https://doi.org/10.1016/j.compstruct.2020.112006

    Article  Google Scholar 

  28. Arora I, Samuel J, Koratkar N (2013) Experimental investigation of the machinability of epoxy reinforced with graphene platelets. J Manuf Sci Eng Trans ASME 135:1–7. https://doi.org/10.1115/1.4024814

    Article  Google Scholar 

  29. Farshbaf Zinati R, Razfar MR (2014) Experimental and modeling investigation of surface roughness in end-milling of polyamide 6/multi-walled carbon nano-tube composite. Int J Adv Manuf Technol 75:979–989. https://doi.org/10.1007/s00170-014-6178-8

    Article  Google Scholar 

  30. Fu G, Huo D, Shyha I et al (2020) Experimental investigation on micromachining of epoxy/graphene nano platelet nanocomposites. Int J Adv Manuf Technol 107:3169–3183. https://doi.org/10.1007/s00170-020-05190-4

    Article  Google Scholar 

  31. Kong D, Chen Y, Li N et al (2019) tool wear estimation in end milling of titanium alloy using NPE and a novel WOA-SVM model. IEEE Trans Instrum Meas 69:5219–5232. https://doi.org/10.1109/tim.2019.2952476

    Article  CAS  ADS  Google Scholar 

  32. Tanvir MH, Hussain A, Rahman MMT et al (2020) Multi-objective optimization of turning operation of stainless steel using a hybrid whale optimization algorithm. J Manuf Mater Process 4:64. https://doi.org/10.3390/jmmp4030064

    Article  CAS  Google Scholar 

  33. Mohanty CP, Mahapatra SS, Singh MR (2017) Effect of deep cryogenic treatment on machinability of Inconel 718 in powder-mixed EDM. Int J Mach Mach Mater 19:343–373. https://doi.org/10.1504/IJMMM.2017.086164

    Article  Google Scholar 

  34. Kesarwani S, Verma RK (2022) Investigation on interface temperature and parametric optimization during machining of reduced graphene oxide nanoflakes dispersed polymer-based carbon fiber reinforced (rGO/CF) nanocomposites. SILICON 14:11181–11198. https://doi.org/10.1007/s12633-022-01844-y

    Article  CAS  Google Scholar 

  35. Liu D, Zhao W, Liu S et al (2016) Comparative tribological and corrosion resistance properties of epoxy composite coatings reinforced with functionalized fullerene C60 and graphene. Surf Coat Technol 286:354–364. https://doi.org/10.1016/j.surfcoat.2015.12.056

    Article  CAS  Google Scholar 

  36. Ogasawara T, Ishida Y, Kasai T (2009) Mechanical properties of carbon fiber/fullerene-dispersed epoxy composites. Compos Sci Technol 69:2002–2007. https://doi.org/10.1016/j.compscitech.2009.05.003

    Article  CAS  Google Scholar 

  37. Khashaba UA, Aljinaidi AA, Hamed MA (2014) Nanofillers modification of Epocast 50–A1/946 epoxy for bonded joints. Chin J Aeronaut 27:1288–1300. https://doi.org/10.1016/j.cja.2014.08.007

    Article  Google Scholar 

  38. Olowojoba GB, Eslava S, Gutierrez ES et al (2016) In situ thermally reduced graphene oxide/epoxy composites: thermal and mechanical properties. Appl Nanosci 6:1015–1022. https://doi.org/10.1007/s13204-016-0518-y

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  39. Goyat MS, Suresh S, Bahl S et al (2015) Thermomechanical response and toughening mechanisms of a carbon nano bead reinforced epoxy composite. Mater Chem Phys 166:144–152. https://doi.org/10.1016/j.matchemphys.2015.09.038

    Article  CAS  Google Scholar 

  40. Pecat O, Rentsch R, Brinksmeier E (2012) Influence of milling process parameters on the surface integrity of CFRP. Procedia CIRP 1:466–470. https://doi.org/10.1016/j.procir.2012.04.083

    Article  Google Scholar 

  41. Xiaohui J, Shan G, Yong Z et al (2021) Prediction modeling of surface roughness in milling of carbon fiber reinforced polymers (CFRP). Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-021-06609-2

    Article  Google Scholar 

  42. Kharwar PK, Verma RK, Mandal NK, Mondal AK (2020) Swarm intelligence integrated approach for experimental investigation in milling of multiwall carbon nanotube/polymer nanocomposites. Arch Mech Eng. https://doi.org/10.24425/ame.2020.131698

    Article  Google Scholar 

  43. Kumar D, Singh KK (2019) Investigation of delamination and surface quality of machined holes in drilling of multiwalled carbon nanotube doped epoxy/carbon fiber reinforced polymer nanocomposite. Proc Inst Mech Eng Part L J Mater Des Appl 233:647–663. https://doi.org/10.1177/1464420717692369

    Article  CAS  Google Scholar 

  44. Thangarasu SK, Shankar S, Mohanraj T, Devendran K (2020) Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network. Proc Inst Mech Eng Part C J Mech Eng Sci 234:329–342. https://doi.org/10.1177/0954406219873932

    Article  CAS  Google Scholar 

  45. Hussain G, Al-Ghamdi KA, Bijanrostami K, Alehashemi AJ (2016) Determination of optimum process parameters for cutting hole in a randomly-oriented glass fiber reinforced epoxy composite by milling process: maximization of surface quality and cut-hole strength. Polym Polym Compos 24:81–89. https://doi.org/10.1177/096739111602400201

    Article  CAS  Google Scholar 

  46. Tazehkandi AH, Shabgard M, Pilehvarian F, Farshfroush N (2017) Experimental investigations of cutting parameters’ influence on cutting forces and surface roughness in turning of Inconel alloy X-750 with biodegradable vegetable oil. Proc Inst Mech Eng Part B J Eng Manuf 231:1516–1527. https://doi.org/10.1177/0954405415599914

    Article  CAS  Google Scholar 

  47. Jagadish, Bhowmik S, Ray A (2015) Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach. J Intell Manuf 30(8):2965–2979. https://doi.org/10.1007/S10845-015-1169-7

    Article  Google Scholar 

  48. Islam MN, Pramanik A (2016) Comparison of design of experiments via traditional and Taguchi method. J Adv Manuf Syst 15:151–160. https://doi.org/10.1142/S0219686716500116

    Article  Google Scholar 

  49. Sampath Kumar T, Balasivanandha Prabu S, Sorna Kumar T (2017) Comparative evaluation of performances of TiAlN-, AlCrN- and AlCrN/TiAlN-coated carbide cutting tools and uncoated carbide cutting tools on turning EN24 alloy steel. J Adv Manuf Syst 16:237–261. https://doi.org/10.1142/S0219686717500159

    Article  Google Scholar 

  50. Ly DK, Truong TT, Nguyen-Thoi T (2021) Multi-objective optimization of laminated functionally graded carbon nanotube reinforced composite plates using deep feedforward neural networks-NSGAII algorithm. Int J Comput Methods. https://doi.org/10.1142/S0219876221500651

    Article  Google Scholar 

  51. Kalita K, Dey P, Haldar S, Gao XZ (2020) Optimizing frequencies of skew composite laminates with metaheuristic algorithms. Eng Comput 36:741–761. https://doi.org/10.1007/s00366-019-00728-x

    Article  Google Scholar 

  52. Nehdi ML, Keshtegar B, Zhu S-P (2019) Nonlinear modeling for bar bond stress using dynamical self-adjusted harmony search optimization. Eng Comput. https://doi.org/10.1007/s00366-019-00831-z

    Article  Google Scholar 

  53. Fountas NA, Vaxevanidis NM, Stergiou CI, Benhadj-Djilali R (2017) A virus-evolutionary multi-objective intelligent tool path optimization methodology for 5-axis sculptured surface CNC machining. Eng Comput 33:375–391. https://doi.org/10.1007/s00366-016-0479-5

    Article  Google Scholar 

  54. Gao S, Li X, Zhang Y, Wang J (2021) A soft-sensor model of VCM rectification concentration based on an improved WOA-RBFNN. Meas Sci Technol 32:085104. https://doi.org/10.1088/1361-6501/abf8ed

    Article  CAS  ADS  Google Scholar 

  55. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  56. Mirjalili S, Song Dong J, Lewis A (2020) Nature-inspired optimizers. Springer, Cham

    Book  Google Scholar 

  57. Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039

    Article  Google Scholar 

  58. Wang J, Du P, Niu T, Yang W (2017) A novel hybrid system based on a new proposed algorithm—multi-objective whale optimization algorithm for wind speed forecasting. Appl Energy 208:344–360. https://doi.org/10.1016/j.apenergy.2017.10.031

    Article  ADS  Google Scholar 

  59. Chen H, Meng Z, Zhou H (2020) A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection. Int J Comput Methods 17:1850145. https://doi.org/10.1142/S0219876218501451

    Article  MathSciNet  Google Scholar 

  60. Huang PTB, Zhang HJ, Lin YC (2019) Development of a Grey online modeling surface roughness monitoring system in end milling operations. J Intell Manuf 30:1923–1936. https://doi.org/10.1007/s10845-017-1361-z

    Article  Google Scholar 

  61. Sindhu D, Thakur L, Chandna P (2019) Multi-objective optimization of rotary ultrasonic machining parameters for quartz glass using Taguchi–Grey relational analysis (GRA). SILICON 11:2033–2044. https://doi.org/10.1007/s12633-018-0019-6

    Article  CAS  Google Scholar 

  62. Kumar D, Singh KK (2016) An experimental investigation of surface roughness in the drilling of MWCNT doped carbon/epoxy polymeric composite material. IOP Conf Ser Mater Sci Eng 149:012096. https://doi.org/10.1088/1757-899X/149/1/012096

    Article  Google Scholar 

  63. Wang H, Sun J, Li J et al (2015) Evaluation of cutting force and cutting temperature in milling carbon fiber-reinforced polymer composites. Int J Adv Manuf Technol 82(9):1517–1525. https://doi.org/10.1007/S00170-015-7479-2

    Article  Google Scholar 

  64. Samuel J, Dikshit A, DeVor RE et al (2009) Effect of carbon nanotube (CNT) loading on the thermomechanical properties and the machinability of CNT-reinforced polymer composites. J Manuf Sci Eng Trans ASME 131:0310081–0310089. https://doi.org/10.1115/1.3123337

    Article  Google Scholar 

  65. Chegdani F, El Mansori M (2019) Tribo-functional effects of double-crossed helix on surface finish, cutting friction and tool wear mechanisms during the milling process of natural fiber composites. Wear 426–427:1507–1514. https://doi.org/10.1016/J.WEAR.2018.11.026

    Article  Google Scholar 

  66. Wang H, Sun J, Zhang D et al (2016) The effect of cutting temperature in milling of carbon fiber reinforced polymer composites. Compos Part A Appl Sci Manuf 91:380–387. https://doi.org/10.1016/j.compositesa.2016.10.025

    Article  CAS  Google Scholar 

  67. Niharika ABP, Khan IA, Khan ZA (2016) Effects of cutting parameters on quality of surface produced by machining of titanium alloy and their optimization. Arch Mech Eng 63:531–548. https://doi.org/10.1515/meceng-2016-0030

    Article  Google Scholar 

  68. Kumar NS, Shetty A, Shetty A et al (2012) Effect of spindle speed and feed rate on surface roughness of carbon steels in CNC turning. Procedia Eng 38:691–697. https://doi.org/10.1016/j.proeng.2012.06.087

    Article  CAS  Google Scholar 

  69. Akinlabi ET, Mathoho I, Mubiayi MP, et al (2018) 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). IEEE, pp 144–147

  70. Paulo Davim J, Silva LR, Festas A, Abrão AM (2009) Machinability study on precision turning of PA66 polyamide with and without glass fiber reinforcing. Mater Des 30:228–234. https://doi.org/10.1016/j.matdes.2008.05.003

    Article  CAS  Google Scholar 

  71. Cha J, Kim J, Ryu S, Hong SH (2019) Comparison to mechanical properties of epoxy nanocomposites reinforced by functionalized carbon nanotubes and graphene nanoplatelets. Compos Part B Eng 162:283–288. https://doi.org/10.1016/j.compositesb.2018.11.011

    Article  CAS  Google Scholar 

  72. Li C, Xiao Q, Tang Y, Li L (2016) A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. J Clean Prod 135:263–275. https://doi.org/10.1016/j.jclepro.2016.06.097

    Article  Google Scholar 

  73. Yan J, Li L (2013) Multi-objective optimization of milling parameters-the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471. https://doi.org/10.1016/j.jclepro.2013.02.030

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the kind support of the Council of Science and Technology, Lucknow, India.

Funding

This work is performed under the R&D project scheme of the Council of Science and Technology Lucknow, India [R&D project ID-UPCST/ D-2491].

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

    Shivi Kesarwani

  2. Department of Mechanical Engineering, School of Engineering, Harcourt Butler Technical University, Kanpur, 208002, India

    Rajesh Kumar Verma

  3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Jinyang Xu

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Kesarwani, S., Verma, R.K. & Xu, J. Modified whale intelligence algorithm and Combined Compromise Solution (CoCoSo) for machinability evaluation of polymer nanocomposites. J Braz. Soc. Mech. Sci. Eng. 46, 66 (2024). https://doi.org/10.1007/s40430-023-04632-w

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