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Exploration of nature inspired Grey wolf algorithm and Grey theory in machining of multiwall carbon nanotube/polymer nanocomposites

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Abstract

The nanosize of reinforcement creates a better synergistic effect in epoxy composites. This effect enhances the desired properties of multifunctional components used in aircraft, automotive, sports, biomedical, sensors, etc. In this series, MWCNT/epoxy nanocomposites possess high strength, reduced weight, moisture and chemical resistant properties. The machining performance of MWCNT nanocomposites is remarkably distinct from metallic materials. This article explores the machining aspects of MWCNT composites using Grey relation analysis and relatively advanced nature-inspired Grey wolf optimization (GWO). Machining (milling) experiments consider a real-world problem framed on Taguchi L27 OA. The second-order polynomial regression equation shows a desirable agreement between experimental and predicted values. The average surface roughness, cutting force and MRR significantly achieved the desired enhancements. The optimal setting of GRA based Grey wolf optimizer are found as 0.5 wt.% MWCNT, 1500 rpm spindle speed, 50 mm/min feed rate and 3 mm depth of cut. It was noticed that the depth of cut plays a prominent role in affecting the machining characteristics. The outcomes of the confirmatory test show that GRA-GWO is more feasible than the traditional GRA method.

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Abbreviations

MWCNT:

Multi wall carbon nanotube

f-MWCNT:

Functionalized multi wall carbon nanotube

GRA:

Grey relational analysis

OA:

Orthogonal array

WPCA:

Weighted principal component analysis

MADM:

Multi attribute decision making

TOPSIS:

The Technique for Order of Preference by Similarity to ideal solution

EDM:

Electro discharge machining

GWOA:

Grey wolf optimization algorithm

CNT:

Carbon nano tube

CNO:

Carbon nano onions

CNR:

Carbon nano rods

CNF:

Carbon nano fibers

XRD:

X-ray diffraction

SEM:

Scanning electron microscope

CNC:

Computerized numeric control

GA:

Genetic algorithm

PSO:

Particle swarm optimization

ACO:

Ant colony optimization

MRR:

Material removal rate

SR:

Surface roughness

F c :

Cutting force

References

  1. Shariati A, Qaderi S, Ebrahimi F et al (2020) On buckling characteristics of polymer composite plates reinforced with graphene platelets. Eng Comput. https://doi.org/10.1007/s00366-020-00992-2

    Article  Google Scholar 

  2. Ahmad Z, Ansell MP, Smedley D, Tahir PMD (2011) Nanoparticles filled epoxy-based adhesive for in situ timber bonding, advances in materials science: composites and nanocomposites, 4. ISBN 13: 978-1-4665-6876-1

  3. Zare Y, Rhee KY (2020) Effect of interfacial/interphase conductivity on the electrical conductivity of polymer carbon nanotubes nanocomposites. Eng Comput. https://doi.org/10.1007/s00366-020-01062-3

    Article  Google Scholar 

  4. Khan W, Sharma R, Saini P (2016) Carbon nanotube-based polymer composites : synthesis, properties and applications, carbon nanotubes. Curr Prog Polym Compos. https://doi.org/10.5772/62497

    Article  Google Scholar 

  5. Pikhurov D, Zuev V (2016) The study of mechanical and tribological performance of fulleroid materials filled PA 6 composites. Lubricants 4(2):13–24. https://doi.org/10.3390/lubricants4020013

    Article  Google Scholar 

  6. Choudhary CW et al (2014) Carbon nanomaterials: a review, handbook of nanomaterials properties. Springer, Berlin, pp 709–769. https://doi.org/10.1007/978-3-642-31107-9_37

    Book  Google Scholar 

  7. Chiu WC, Huang CL (2016) Rheological and conductivity percolations of syndiotactic polystyrene composites filled with graphene nanosheets and carbon nanotubes: a comparative study. Compos Sci Technol 134(1):53–60

    Google Scholar 

  8. Taylor P, Siddiq M, Kausar A, Ashraf R, Siddiq M (2014) Polymer/nanodiamond composites in li-ion batteries: a review. Polym Plast Technol Eng 53:550–563

    Google Scholar 

  9. Srikanth I, Kumar S, Kumar A, Ghosal P, Subrahmanyam C (2012) Composites: part A effect of amino-functionalized MWCNT on the crosslink density, fracture toughness of epoxy and mechanical properties of carbon—epoxy composites. Compos A 43(11):2083–2086

    Google Scholar 

  10. Wu X, Han Y, Zhang X, Lu C (2016) Highly sensitive, stretchable, and wash-durable strain sensor based on ultrathin conductive layer polyurethane yarn for tiny motion monitoring. ACS Appl Mater Interfaces 8(15):9936–9945

    Google Scholar 

  11. Zhao J, Dai K, Liu C, Zheng G, Wang B (2003) A comparison between strain sensing behaviors of carbon black/polypropylene and carbon nanotubes/polypropylene electrically conductive composites. Compos Part A 48:129–136

    Google Scholar 

  12. Liu H, Li Y, Dai K, Zheng G, Liu C, Shen C (2016) Electrically conductive thermoplastic elastomer nanocomposites at ultralow graphene loading levels for strain sensor applications. J Mater Chem 4(1):157–166

    Google Scholar 

  13. Lin L, Liu S, Fu S, Zhang S, Deng H, Fu Q (2013) fabrication of highly stretchable conductors via morphological control of carbon nanotube network. Small 9(21):3620–3629

    Google Scholar 

  14. Eswaraiah V, Balasubramaniam K, Ramaprabhu S (2011) Functionalized graphene reinforced thermoplastic nanocomposites as strain sensors in structural health monitoring. J Mater Chem 21(34):12626–12628

    Google Scholar 

  15. Ding D, Wei H, Zhu J, He Q, Yan X, Wei S (2014) Strain sensitive polyurethane nanocomposites reinforced with multiwalled carbon nanotubes. Energy Environ Focus 3(1):85–93

    Google Scholar 

  16. Li D, Liu Y, Lin B, Lai C, Sun Y, Yang H (2015) synthesis of ternary graphene/molybdenum oxide/poly(p-phenylenediamine) nanocomposites for symmetric supercapacitors. RSC Adv 5(119):98278–98287

    Google Scholar 

  17. Gupta TK, Singh BP, Dhakate SR, Singh VN, Mathur RB (2013) Improved nanoindentation and micro-wave shielding properties of modified MWCNT reinforced polyurethane composites. J Mater Chem 10(32):9138–9149

    Google Scholar 

  18. Gupta TK, Singh BP, Teotia S, Katyal V, Dhakate SR, Mathur RB (2013) Designing of multiwalled carbon nanotubes reinforced polyurethane composites as electromagnetic interference shielding materials. J Polym Res 20(6):1–7

    Google Scholar 

  19. Gupta TK, Singh BP, Mathur RB, Dhakate SR (2014) Multi-walled carbon nanotube-graphene-polyaniline multiphase nanocomposite with superior electromagnetic shielding effectiveness. Nanoscale 6(2):842–851

    Google Scholar 

  20. De Volder MF, Tawfick SH, Baughman RH, Hart AJ (2013) Carbon nanotubes: present and future commercial applications. Science 339(6119):535–539

    Google Scholar 

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

    Google Scholar 

  22. Marconne AMT, Yamamoto N, Panzer MA, Wardle BL, Oodson KEG (2011) Thermal conduction in aligned carbon nanotube–polymer nanocomposites with high packing density. ACS Nano 5(6):4818–4825

    Google Scholar 

  23. Dixit S, Mahata A, Mahapatra DR, Kailas SV, Chattopadhyay K (2018) Multi-layer graphene reinforced aluminum—manufacturing of high strength composite by friction stir alloying. Compos Part B Eng 136:63–71. https://doi.org/10.1016/j.compositesb.2017.10.028

    Article  Google Scholar 

  24. Kostagiannakopoulou C, Tsilimigkra X, Sotiriadis G, Kostopoulos V (2017) Synergy effect of carbon nano-fillers on the fracture toughness of structural composites. Compos Part B Eng 129:18–25. https://doi.org/10.1016/j.compositesb.2017.07.012

    Article  Google Scholar 

  25. Kavimani V, Prakash KS (2018) Doping effect of SiC over graphene on dry sliding wear behaviour of Mg/SiC@r-GO MMCs and its surface characterization. Silicon 10:2829–2843. https://doi.org/10.1007/s12633-018-9823-2

    Article  Google Scholar 

  26. Cheung W, Pontoriero F, Taratula O, Chen AM, He H (2010) DNA and carbon nanotubes as medicine. Adv Drug Deliv Rev 62(6):633–649

    Google Scholar 

  27. Schadler LS, Giannaris SC, Ajayan PM (1998) Load transfer in carbon nanotubes epoxy composites. Appl Phys Lett 73:3842–3844

    Google Scholar 

  28. Vajaiac E et al (2015) Mechanical properties of multiwall carbon nanotube-epoxy composites. Digest J Nanomater Biostruct 10(2):359–369

    Google Scholar 

  29. Nguyen TA, Nguyen QT, Bach TP (2019) Mechanical properties and flame retardancy of epoxy resin/nanoclay/multiwalled carbon nanotube nanocomposites. J Chem Article ID 3105205. https://doi.org/10.1155/2019/3105205

    Article  Google Scholar 

  30. Guo P, Chen X, Gao X, Song H, Shen H (2007) Fabrication and mechanical properties of nanotubes/epoxy composites. Compos Sci Technol 67:3331–3337

    Google Scholar 

  31. Gantayat S, Rout D, Swain SK (2017) Mechanical properties of functionalized multiwalled carbon nanotube/epoxy nanocomposites. Mater Today Proc 4(2):4061–4064

    Google Scholar 

  32. Montazeri A, Khavandi A, Javadpour J, Tcharkhtchi A (2010) Viscoelastic properties of multiwalled carbon nanotube/epoxy composites using two different curing cycles. Mater Des 31:3383–3388

    Google Scholar 

  33. Allaoui A, Bai S, Cheng HM, Bai JB (2002) Mechanical and electrical properties of an MWNT/epoxy composite. Compos Sci Technol 62(15):1993–1998

    Google Scholar 

  34. Kumar MN, Mahmoodi M, Tabkhpaz M, Park SS, Jin X (2017) Characterization and micro end milling of graphene nano platelet and carbon nanotube filled nanocomposites. J Mater Process Technol 249:96–107. https://doi.org/10.1016/j.jmatprotec.2017.06.005

    Article  Google Scholar 

  35. Altin KM, Gökkaya H (2018) A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Def Technol 14(4):318–326

    Google Scholar 

  36. Kumar D, Akhtar N, Himadri K, Rajiv M, Garg K (2018) Experimental investigation of the PMEDM of nickel free austenitic stainless steel: a promising coronary stent material. Silicon 11:899–907. https://doi.org/10.1007/s12633-018-9877-1

    Article  Google Scholar 

  37. Naveen AS, Aravindan S, Noorul HA (2009) Optimisation of machining parameters of glass-fiber-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi technique. Int J Adv Manuf Technol 43(5–6):581–589

    Google Scholar 

  38. Nair A, Kumanan S (2016) Multi performance optimization of abrasive water jet machining of Inconel 617 using WPCA. Mater Manuf 32(6):693–699

    Google Scholar 

  39. Yiyo K, Taho YB, Guan-Wei H (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55:80–93

    Google Scholar 

  40. Olson DL, Desheng W (2006) Simulation of fuzzy multiattribute models for grey relationships. Eur J Oper Res 175:111–120

    MATH  Google Scholar 

  41. Jiang CC, Tasi BC (2002) Machine vision-based Grey relational theory applied to IC marking inspection. IEEE Trans Semicond Manuf 15:531–553

    Google Scholar 

  42. Wu H (2006) A comparative study of using Grey relational analysis in multiple attribute decision-making problems. Qual Eng 15(2):209–217

    Google Scholar 

  43. Chin TL, Che WC, Chie BC (2006) The worst ill-conditioned silicon wafer slicing machine detected by using grey relational analysis. Int J Adv Manuf Technol 31:388–395

    Google Scholar 

  44. Tarng YS, Juang SC, Chang CH (2002) The use of Grey-based Taguchi methods to determine submerged arc welding process parameters in hard facing. J Mater Process Technol 128:1–6

    Google Scholar 

  45. Kao PS, Hocheng H (2003) Optimization of electrochemical polishing of stainless steel by Grey relational analysis. J Mater Process Technol 140:255–259

    Google Scholar 

  46. Lin JL, Lin JF (2006) Grey theory applied to evaluate the tribological performances of the aC: H (N). Int J Adv Manuf Technol 27:845–853

    Google Scholar 

  47. Lin JL, Lin CL (2002) The use of the orthogonal array with Grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics. Int J Mach Tools Manuf 42:237–244

    Google Scholar 

  48. Suvvari A, Goyari P (2019) Financial performance assessment using Grey relational analysis (GRA): an application to life insurance companies in India. Grey Syst Theory Appl 9(4):502–516. https://doi.org/10.1108/GS-05-2019-0010

    Article  Google Scholar 

  49. Kalyon A, Günay M, Özyürek D (2018) Application of Grey relational analysis based on Taguchi method for optimizing machining parameters in hard turning of high chrome cast iron. Adv Manuf 6:419–429. https://doi.org/10.1007/s40436-018-0231-z

    Article  Google Scholar 

  50. Kwang L, Chung C, Long S, Feng H (2007) Optimizing multiple quality characteristics via Taguchi method-based Grey analysis. J Mater Process Technol 182:107–116

    Google Scholar 

  51. Singh PN, Raghukandan K, Pai BC (2004) Optimization by Grey relational analysis of EDM parameters on machining Al—10% SiC P composites. J Mater Process Technol 156:1658–1661

    Google Scholar 

  52. Nikolaos MV, Kechagias JD, Nikolaos A, Manolakos DE (2013) Three component cutting force system modeling and optimization in turning of AISI D6 tool steel using design of experiments and neural networks. In: Proceedings of the world congress on engineering, I, July 3–5, 2013, London, UK

  53. Fountas NA, Ntziantzias I, Vaxevanidis NM (2018) Multi-objective optimization of cutting parameters for drilling pa66-gf30 glass fiber reinforced polyamide by employing genetic algorithms. J Manuf Technol Res 10(1–2):1–16

    Google Scholar 

  54. Amir HG, Xin-She Y, Siamak T, Amir HA (2013) Metaheuristic algorithms in modeling and optimization metaheuristic algorithms in modeling and optimization. Metaheuristic Appl Struct Infrastruct. https://doi.org/10.1016/B978-0-12-398364-0.00001-2

    Article  Google Scholar 

  55. Nikolaos MV, Provatidis CG, Fountas NA, Manolakos DE, Seretis GV (2018) Multi-objective statistical analysis and optimisation in turning of aluminium matrix particulate composite using genetic algorithms. Int J Mach Mach Mater 20(3):236–251. https://doi.org/10.1504/ijmmm.2018.10014645

    Article  Google Scholar 

  56. Abidi MH, Al-Ahmari AM, Umer U, Rasheed MS (2018) Multi-objective optimization of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using MOGA-II. Meas J Int Meas Confed 125:336–349

    Google Scholar 

  57. Fountas NA, Vaxevanidis NM (2020) Intelligent 3D tool path planning for optimized 3-axis sculptured surface CNC machining through digitized data evaluation and swarm-based evolutionary algorithms. Measurement 158:107678. https://doi.org/10.1016/j.measurement.2020.107678

    Article  Google Scholar 

  58. Ravi SB, Umamaheswarrao P (2018) Multi-objective optimization of CFRP composite drilling using ant colony algorithm. Mater Today Proc 5(2):4855–4860

    Google Scholar 

  59. Madic M, Radovanovic M (2013) Application of cuckoo search algorithm for surface roughness optimization in Co2 laser. Ann Fac Eng Hunedoara Int J Eng 11:39–44 (ISSN 1584-2665)

    Google Scholar 

  60. Mahanta GB, Rout A, Gunji B, Deepak BBVL, Biswal BB (2020) Multi-objective design optimization of a bioinspired underactuated robotic gripper using multi-objective Grey wolf optimizer. In: Biswal B, Sarkar B, Mahanta P (eds) Advances in mechanical engineering. Lecture notes in mechanical engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0124-1_131

    Chapter  Google Scholar 

  61. Misra A, Pandey PM, Dixit US, Roy A, Silberschmidt VV (2018) Multi-objective optimization of the ultrasonic-assisted magnetic abrasive finishing process. Int J Adv Manuf Technol 101:1661–1670. https://doi.org/10.1007/s00170-018-3060-0

    Article  Google Scholar 

  62. Lmalghan R, Rao K, Arun SK, Rao SS, Herbert MA (2018) Machining parameters optimization of AA6061 using response surface methodology and particle swarm optimization. Int J Precis Eng Manuf 19(5):695–704

    Google Scholar 

  63. Fountas NA, Vaxevanidis NM, Stergiou CI et al (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 

  64. Fan Q, Chen Z, Li Z et al (2020) A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems. Eng Comput. https://doi.org/10.1007/s00366-019-00917-8

    Article  Google Scholar 

  65. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1–4):55–61

    Google Scholar 

  66. Kulkarni O, Kulkarni S (2018) Process parameter optimization in WEDM by Grey wolf optimizer. Mater Today Proc 5(2):4402–4412

    Google Scholar 

  67. Kaveh M, Chayjan RA, Taghinezhad E et al (2019) Modeling of thermodynamic properties of carrot producing using ALO GWO and WOA algorithm under multi-stage semi-industrial continuous belt dryer. Eng Comput 25:1045–1058

    Google Scholar 

  68. Mirjalili S, Mohammad S, Lewis A (2014) Advances in engineering software Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  69. Chakraborty S, Mitra A (2018) Parametric optimization of abrasive water-jet machining processes using Grey wolf optimizer. Mater Manuf Process 33(13):1471–1482

    Google Scholar 

  70. Khalilpourazari S, Khalilpourazary S (2018) Optimization of production time in the multi-pass milling process via a Robust Grey wolf optimizer. Neural Comput Appl 29(12):1321–1336

    Google Scholar 

  71. Fountas N, Koutsomichalis A, Kechagias J, Vaxevanidis N (2019) Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey wolf algorithm. Frattura Ed Integrità Strutturale 13(50):584–594. https://doi.org/10.3221/igf-esis.50.49

    Article  Google Scholar 

  72. Raju M, Gupta MK, Bhanot N (2019) A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. J Intell Manuf 30:2743–2758

    Google Scholar 

  73. Hamed B, Frank C, Mohammad S (2019) Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach. Procedia Manuf 34:961–968

    Google Scholar 

  74. Tripathi S, Shrivastava A, Kartick CJ (2019) Self-Tuning fuzzy controller for sun-tracker system using Gray wolf optimization (GWO) technique. ISA Trans. https://doi.org/10.1016/j.isatra.2020.01.012

    Article  Google Scholar 

  75. Vaxevanidis NM, Kechagias JD, Fountas NA, Manolakos DE (2014) Evaluation of machinability in turning of engineering alloys by applying artificial neural networks. Open Constr Build Technol J 8(1):389–399. https://doi.org/10.2174/1874836801408010389

    Article  Google Scholar 

  76. Bikash B, Ankit A (2019) Concurrent parametric optimization of single-pass end milling through GRA coupled with PSO for Calmax-635 die steel. Int J Swarm Intell 4(1):1–19

    Google Scholar 

  77. Varun A, Venkaiah N (2015) Simultaneous optimization of WEDM responses using Grey relational analysis coupled with genetic algorithm while machining EN 353. Int J Adv Manuf Technol 76:675–690

    Google Scholar 

  78. Senapati NP, Tripathy S, Samantaray S (2016) A multi-objective optimization using a combined approach of principal component analysis and TOPSIS during electric discharge machining of H-11 die steel using P/M processed Cu–Cr–Ni metal matrix composite. In: International conference on electrical, electronics, and optimization techniques, pp 1207–1212

  79. Nikolaos MV, Nikolaos AF, John DK, Manolakos DE (2014) Optimization of main cutting force and surface roughness in turning of TI-6AL-4V titanium alloy using design of experiments and artificial neural networks. In: Proceedings of the 1st international conference on engineering and applied sciences optimization held in Kos Island, Greece 4–6 June 2014

  80. Fountas NA, Ntziantzias I, Kechagias J, Koutsomichalis A, Davim JP, Vaxevanidis NM (2013) Prediction of cutting forces during turning PA66 GF-30 glass fiber reinforced polyamide by soft computing techniques. Mater Sci Forum 766:37–58. https://doi.org/10.4028/www.scientific.net/msf.766.37

    Article  Google Scholar 

  81. Ju-long D (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294

    MathSciNet  MATH  Google Scholar 

  82. Kaushik N, Singhal S (2018) Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiC p matrix composite. Product Manuf Res 6(1):171–189

    Google Scholar 

  83. Li N, Kong YCD (2019) Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based Grey relational analysis coupled with kernel principal component analysis. Adv Manuf 7(2):142–154

    Google Scholar 

  84. Mirjalili S, Saremi S, Mohammad S (2016) Multi-objective Grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Google Scholar 

  85. Muro C, Escobedo R, Spector L, Coppinger RP (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Proc 88(3):192–197

    Google Scholar 

  86. Geetha M, Sreenivasulu B, Harinath G (2013) Modeling & analysis of performance characteristics of wire EDM of SS304. Int J Innov Technol Explor Eng 3(4):122–125 (ISSN: 2278-3075)

    Google Scholar 

  87. Nurhaniza M, Ariffin MK, Mustapha F, Baharudin BT (2016) Analyzing the effect of machining parameters setting to the surface roughness during end milling of CFRP-Aluminium composite laminates. Int J Manuf Eng. https://doi.org/10.1155/2016/4680380

    Article  Google Scholar 

  88. Youliang S, Zhenyuan J, Bin N, Guangjian B (2017) Size effect of depth of cut on chip formation mechanism in machining of CFRP. Compos Struct 164:316–327

    Google Scholar 

  89. Yousefi S, Zohoor M (2019) Effect of cutting parameters on the dimensional accuracy and surface finish in the hard turning of MDN250 steel with cubic boron nitride tool, for developing a knowledge base expert system. Int J Mech Mater Eng 14(1):1–13. https://doi.org/10.1186/s40712-018-0097-7

    Article  Google Scholar 

  90. Kecik K, Ciecielag K, Zaleski K (2017) Damage detection of composite milling process by recurrence plots and quantifications analysis. Int J Adv Manuf Technol 89:133–144. https://doi.org/10.1007/s00170-016-9048-8

    Article  Google Scholar 

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

    Prakhar Kumar Kharwar & Rajesh Kumar Verma

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Kharwar, P.K., Verma, R.K. Exploration of nature inspired Grey wolf algorithm and Grey theory in machining of multiwall carbon nanotube/polymer nanocomposites. Engineering with Computers 38, 1127–1148 (2022). https://doi.org/10.1007/s00366-020-01103-x

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