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A review: use of evolutionary algorithm for optimisation of machining parameters

Abstract

Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve the desired outputs; minimum surface roughness (SR), highest material removal rate (MRR), lowest production cost, and the shortest production time of machining processes and various optimisation attempts in terms of varying parameters that affect the outcomes. The review deliberates the optimisation methods employed and analyses the performance discussing the relevant parameters that must have been considered by past researchers. To date, most studies have been focusing on optimising conventional machining processes such as turning, milling, and drilling. Optimisation works have been performed parametrically, experimentally, and numerically, where discrete variations of the parameters are investigated, while others are remained constant. Lately, evolutionary algorithm, statistical approaches such as genetic algorithm (GA), particle swarm optimisation (PSO), and cuckoo search algorithm (CSA) have been utilised in simultaneous optimisation of the parameters of the desired outputs and its great potential in optimising machining processes is recognisable.

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Data availability

All data generated or analysed during this study are included in this published article (and its supplementary information files).

References

  1. 1.

    Zolpakar NA, Lodhi SS, Pathak S, Sharma MA (2020) Application of multi-objective genetic algorithm (MOGA) optimization in machining processes. https://doi.org/10.1007/978-3-030-19638-7_8

  2. 2.

    Pathak S (2021) Intelligent manufacturing. https://doi.org/10.1007/978-3-030-50312-3

  3. 3.

    Abhishek K, Rakesh Kumar V, Datta S, Mahapatra SS (2017) Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). J Intell Manuf 28:1769–1785

    Google Scholar 

  4. 4.

    Pathak S, Jain NK (2017) Critical review of electrochemical honing: sustainable and alternative gear finishing process. Part 2: effects of various process parameters on surface characteristics and material removal rate. Trans Inst Met Finish 95:241–254

    Google Scholar 

  5. 5.

    Pathak S, Jain NK (2017) Critical review of electrochemical honing (ECH): sustainable and alternative gear finishing process. Part 1: conventional processes and introduction to ECH. Trans Inst Met Finish 95:147–157

    Google Scholar 

  6. 6.

    Shaikh JH, Jain NK, Pathak S (2016) Investigations on surface quality improvement of straight bevel gears by electrochemical honing process. Proc Inst Mech Eng Part B J Eng Manuf 230:1242–1253

    Google Scholar 

  7. 7.

    Gupta MK, Sood PK, Sharma VS (2016) Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. J Clean Prod 135:1276–1288

    Google Scholar 

  8. 8.

    Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37:4650–4659

    Google Scholar 

  9. 9.

    Zainal N, Zain AM, Radzi NHM, Othman MR (2016) Glowworm swarm optimization (GSO) for optimization of machining parameters. J Intell Manuf 27:797–804

    Google Scholar 

  10. 10.

    Mia M, Dey PR, Hossain MS, Arafat MT, Asaduzzaman M, Shoriat Ullah M, Tareq Zobaer SM (2018) Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition. Meas J Int Meas Confed 122:380–391

    Google Scholar 

  11. 11.

    Sangwan KS, Kant G (2017) Optimization of machining parameters for improving energy efficiency using integrated response surface methodology and genetic algorithm approach. Procedia CIRP 61:517–522

    Google Scholar 

  12. 12.

    Pawanr S, Garg GK, Routroy S (2020) Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS. Procedia CIRP 86:116–120

    Google Scholar 

  13. 13.

    Kuntoğlu M, Sağlam H (2019) Investigation of progressive tool wear for determining of optimized machining parameters in turning. Meas J Int Meas Confed 140:427–436

    Google Scholar 

  14. 14.

    Krishnaraj V, Prabukarthi A, Ramanathan A, Elanghovan N, Kumar MS, Zitoune R, Davim JP (2012) Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates. Compos Part B Eng 43:1791–1799

    Google Scholar 

  15. 15.

    Kant G, Sangwan KS (2014) Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J Clean Prod 83:151–164

    Google Scholar 

  16. 16.

    Rajmohan T, Palanikumar K, Kathirvel M (2012) Optimization of machining parameters in drilling hybrid aluminium metal matrix composites. Trans Nonferrous Metals Soc China English Ed 22:1286–1297

    Google Scholar 

  17. 17.

    Wang Q, Liu F, Wang X (2014) Multi-objective optimization of machining parameters considering energy consumption. Int J Adv Manuf Technol 71:1133–1142

    Google Scholar 

  18. 18.

    Maiyar LM, Ramanujam R, Venkatesan K, Jerald J (2013) Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis. Procedia Eng 64:1276–1282

    Google Scholar 

  19. 19.

    Sangwan KS, Saxena S, Kant G (2015) Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP 29:305–310

    Google Scholar 

  20. 20.

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

    Google Scholar 

  21. 21.

    Lodhi BK, Agarwal S (2014) Optimization of machining parameters in WEDM of AISI D3 steel using taguchi technique. Procedia CIRP 14:194–199

    Google Scholar 

  22. 22.

    Aliakbari E, Baseri H (2012) Optimization of machining parameters in rotary EDM process by using the Taguchi method. Int J Adv Manuf Technol 62:1041–1053

    Google Scholar 

  23. 23.

    Rajmohan T, Palanikumar K (2013) Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites. Meas J Int Meas Confed 46:1470–1481

    Google Scholar 

  24. 24.

    Zhou G, Lu Q, Xiao Z, Zhou C, Tian C (2019) Cutting parameter optimization for machining operations considering carbon emissions. J Clean Prod 208:937–950

    Google Scholar 

  25. 25.

    Sathish T (2019) Experimental investigation of machined hole and optimization of machining parameters using electrochemical machining. J Mater Res Technol 8:4354–4363

    Google Scholar 

  26. 26.

    Mirkoohi E, Bocchini P, Liang SY (2019) Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102:1557–1566

    Google Scholar 

  27. 27.

    Wang G, Li W, Rao F, He Z, Yin Z (2019) Multi-parameter optimization of machining impeller surface based on the on-machine measuring technique. Chin J Aeronaut 32:2000–2008

    Google Scholar 

  28. 28.

    Camposeco-Negrete C (2019) Prediction and optimization of machining time and surface roughness of AISI O1 tool steel in wire-cut EDM using robust design and desirability approach. Int J Adv Manuf Technol 103:2411–2422

    Google Scholar 

  29. 29.

    Azlan Suhaimi M, Park KH, Sharif S, Kim DW, Saladin Mohruni A (2017) Evaluation of cutting force and surface roughness in high-speed milling of compacted graphite iron. MATEC Web Conf 101. https://doi.org/10.1051/matecconf/201710103016

  30. 30.

    Rajmohan T (2019) Experimental investigation and optimization of machining parameters in drilling of fly ash-filled carbon fiber reinforced composites. Part Sci Technol 37:21–30

    Google Scholar 

  31. 31.

    Vijayabhaskar S, Rajmohan T (2019) Experimental investigation and optimization of machining parameters in WEDM of nano-SiC particles reinforced magnesium matrix composites. Silicon 11:1701–1716

    Google Scholar 

  32. 32.

    Mahesh G, Muthu S, Devadasan SR (2015) Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77:369–381

    Google Scholar 

  33. 33.

    Khare SK, Phull GS, Verma RK, Agarwal S (2020) A comparison between optimization techniques of cutting parameters under cryogenic machining process. Mater Today Proc 26:2697–2700. https://doi.org/10.1016/j.matpr.2020.02.567

    Article  Google Scholar 

  34. 34.

    Maneiah D, Shunmugasundaram M, Raji Reddy A, Begum Z (2020) Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites. Mater Today Proc 27:1293–1298. https://doi.org/10.1016/j.matpr.2020.02.264

    Article  Google Scholar 

  35. 35.

    Karthik Pandiyan G, Prabaharan T (2020) Optimization of machining parameters on AA6351 alloy steel using Response Surface Methodology (RSM). Mater Today Proc:1–4

  36. 36.

    Zubair AF, Abu Mansor MS (2019) Embedding firefly algorithm in optimization of CAPP turning machining parameters for cutting tool selections. Comput Ind Eng 135:317–325

    Google Scholar 

  37. 37.

    Fazlollahtabar H, Gholizadeh H (2020) Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters. Comput Ind Eng 140:106225

    Google Scholar 

  38. 38.

    Chethan YD, Ravindra HV, Krishnegowda YT (2019) Optimization of machining parameters in turning Nimonic-75 using machine vision and acoustic emission signals by Taguchi technique. Meas J Int Meas Confed 144:144–154

    Google Scholar 

  39. 39.

    Leo Kumar SP (2018) Experimental investigations and empirical modeling for optimization of surface roughness and machining time parameters in micro end milling using Genetic Algorithm. Meas J Int Meas Confed 124:386–394

    Google Scholar 

  40. 40.

    Sathiya Narayanan N, Baskar N, Ganesan M (2018) Multi objective optimization of machining parameters for hard turning OHNS/AISI H13 material, using genetic algorithm. Mater Today Proc 5:6897–6905

    Google Scholar 

  41. 41.

    Dhavamani C, Alwarsamy T (2012) Optimization of machining parameters for aluminum and silicon carbide composite using genetic algorithm. Procedia Eng 38:1994–2004

    Google Scholar 

  42. 42.

    Reddy VC, Gowd GH, Kumar MLSD (2018) Empirical modeling & optimization of laser micro - machining process parameters using genetic algorithm. Mater Today Proc 5:8095–8103

    Google Scholar 

  43. 43.

    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. Meas J Int Meas Confed 158:107678

    Google Scholar 

  44. 44.

    Manav O, Chinchanikar S (2018) Multi-objective optimization of hard turning: a genetic algorithm approach. Mater Today Proc 5:12240–12248

    Google Scholar 

  45. 45.

    Selvam MD, Karuppusami G, Dawood AKS (2012) Optimization of machining parameters for face milling operation in a vertical cnc milling machine using genetic algorithm. An Int J ESTIJ 2:2250–3498

    Google Scholar 

  46. 46.

    Zhang W, Zhang L, Wang S, Ren B, Meng S (2019) Optimization of machining parameters of 2.25Cr1Mo0.25V steel based on response surface method and genetic algorithm. Int J Interact Des Manuf 13:809–819

    Google Scholar 

  47. 47.

    Umer U, Mohammed MK, Al-Ahmari A (2017) Multi-response optimization of machining parameters in micro milling of alumina ceramics using Nd:YAG laser. Meas J Int Meas Confed 95:181–192

    Google Scholar 

  48. 48.

    Gupta I, Tyagi G (2017) Optimization of machining parameters in electrical discharge machining process of Ti-6Al-4V alloy by Taguchi method. 3:44–50

  49. 49.

    Nain SS, Garg D, Kumar S (2018) Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization. Eng Sci Technol an Int J 21:261–273

    Google Scholar 

  50. 50.

    Manav O, Chinchanikar S, Gadge M (2018) Multi-performance optimization in hard turning of AISI 4340 steel using particle swarm optimization technique. Mater Today Proc 5:24652–24663

    Google Scholar 

  51. 51.

    Sridhar R, Perumal Subramaniyan S, Ramesh S (2019) Optimization of machining and geometrical parameters to reduce vibration while milling metal matrix composite. Trans Indian Inst Metals 72:3179–3189

    Google Scholar 

  52. 52.

    Gopalakrishnan R, John ERD (2017) Experimental investigation and multi response optimization of WEDM process of AA7075 metal matrix composites using particle swarm optimization. Int J Intell Eng Syst 10:166–174

    Google Scholar 

  53. 53.

    Marko H, Simon K, Tomaz I, Matej P, Joze B, Miran B (2014) Turning parameters optimization using particle swarm optimization. Procedia Eng 69:670–677

    Google Scholar 

  54. 54.

    Sreenivasa Rao M, Venkaiah N (2015) Parametric optimization in machining of nimonic-263 alloy using RSM and particle swarm optimization. Procedia Mater Sci 10:70–79

    Google Scholar 

  55. 55.

    Malghan RL, Rao KMC, Shettigar AK, Rao SS, D’Souza RJ (2017) Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. J Braz Soc Mech Sci Eng 39:3541–3553

    Google Scholar 

  56. 56.

    Jabri A, El Barkany A, El Khalfi A (2017) Multipass turning operation process optimization using hybrid genetic simulated annealing algorithm. Model Simul Eng 2017:1–10. https://doi.org/10.1155/2017/1940635

    Article  Google Scholar 

  57. 57.

    Varatharajulu M, Loganathan C, Baskar N (2015) Influence of cutting parameters on roughness and roundness in drilling of duplex 2205 using high speed steel. Int J Appl Eng Res 10:129–136

    Google Scholar 

  58. 58.

    Sathish T (2018) BCCS approach for the parametric optimization in machining of nimonic-263 alloy using RSM. Mater Today Proc 5:14416–14422

    Google Scholar 

  59. 59.

    Mohamad A, Zain AM, Mohd Yusof N, Najarian F, Alwee R, Abdull Hamed HN (2019) Modeling and optimization of machining parameters using regression and cuckoo search in deep hole drilling process. Appl Mech Mater 892:177–184

    Google Scholar 

  60. 60.

    Huang J, Gao L, Li X (2015) An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Appl Soft Comput J 36:349–356

    Google Scholar 

  61. 61.

    Saravanan M, Thiyagarajan C, Somasundaram S (2020) Parametric optimization of wirecut-electrical discharge machining through cuckoo search algorithm. Mater Today Proc 22:681–687

    Google Scholar 

  62. 62.

    Priti SM, Singh S (2020) Materials today : proceedings micro-machining of CFRP composite using electrochemical discharge machining and process optimization by Entropy-VIKOR method. Mater Today Proc 44:260–265. https://doi.org/10.1016/j.matpr.2020.09.463

    Article  Google Scholar 

  63. 63.

    Singh T, Rathore RS, Dvivedi A (2020) Experimental investigations, empirical modeling and multi objective optimization of performance characteristics for ECDD with pressurized feeding method. Measurement 149:107017

    Google Scholar 

  64. 64.

    Reddy BS, Rao ABK, Janardhana GR (2020) Multi-objective optimization of surface roughness, recast layer thickness and surface crack density in WEDM of Al2124/SiCp using desirability approach. Mater Today Proc 39:1320–1326. https://doi.org/10.1016/j.matpr.2020.04.563

    Article  Google Scholar 

  65. 65.

    Thakur RK, Singh KK (2020) Experimental investigation and optimization of abrasive water jet machining parameter on multi-walled carbon nanotube doped epoxy/carbon laminate. Measurement 2020:108093

    Google Scholar 

  66. 66.

    Balaji K, Kumar MS, 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 J 102:107075

    Google Scholar 

  67. 67.

    Kumar KR, Sreebalaji VS, Pridhar T (2017) Characterization and optimization of abrasive water jet machining parameters of aluminium /tungsten carbide composites. Measurement. 117:57–66. https://doi.org/10.1016/j.measurement.2017.11.059

    Article  Google Scholar 

  68. 68.

    Shukla R, Singh D (2016) Author’s Accepted Manuscript. Swarm Evol Comput 32:167–183. https://doi.org/10.1016/j.swevo.2016.07.002

    Article  Google Scholar 

  69. 69.

    Gostimirovic M, Pucovsky V, Sekulic M, Rodic D, Pejic V (2018) Evolutionary optimization of jet lag in the abrasive water jet machining.

  70. 70.

    Acharya BR, Mohanty CP, Mahapatra SS (2013) Multi-objective optimization of electrochemical machining of hardened steel using NSGA II. Procedia Eng 51:554–560

    Google Scholar 

  71. 71.

    Sohrabpoor H, Khanghah SP, Shahraki S, Teimouri R (2016) Multi-objective optimization of electrochemical machining process, pp 1683–1692

    Google Scholar 

  72. 72.

    Santhi M (2013) Optimization of process parameters in electro chemical machining ( ECM ) using DFA-fuzzy set theory-TOPSIS for titanium alloy. Multidiscip Model Mater Struct 9:243–255

    Google Scholar 

  73. 73.

    Mehrvar A, Basti A, Jamali A (2016) Optimization of electrochemical machining process parameters : combining response surface methodology and differential evolution algorithm. J Process Mech Eng 1:1–13

  74. 74.

    Mariapushpam T, Jegan C (2017) Electrochemical machining process parameter optimization using particle swarm optimization. Comput Intell:1–19

  75. 75.

    Sathiyamoorthy V, Sekar T, Elango N (2015) Optimization of processing parameters in ECM of die tool steel using nanofluid by multiobjective genetic algorithm. Sci World J 2015:1–7

    Google Scholar 

  76. 76.

    Holland JH (1957) Adaptation in natural An introductory analysis with applications to biology, 3 pages

    Google Scholar 

  77. 77.

    Deb K (2001) Multi-objective optimization using evolutionary algorithms: an introduction multi-objective optimization using evolutionary algorithms: an introduction. Wiley-Interscience Ser Syst Optim, pp 1–24

  78. 78.

    Alberto I, Azcarate C, Mallor F, Mateo PM (2003) Multiobjective evolutionary algorithms. Pareto Rankings. Proc VII Jornadas Zaragoza-Pau Matemática Apl y Estadística Monogr del Semin Mat Garcia Galdeano no27 27:27–35

  79. 79.

    Eberhart JKCR (2AD) Particle swarm optimization. Adapt Learn Optim 15:45–82

  80. 80.

    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. 2009 World Congr Nat Biol Inspired Comput NABIC 2009 - Proc 210–214

  81. 81.

    Joshi AS, Kulkarni O, Kakandikar GM, Nandedkar VM (2017) Cuckoo Search Optimization—a review. Mater Today Proc 4:7262–7269

    Google Scholar 

  82. 82.

    Khoja I, Ladhari T, Sakly A, M’Sahli F (2018) Parameter identification of an activated sludge wastewater treatment process based on particle swarm optimization method. Math Probl Eng 2018:1–11. https://doi.org/10.1155/2018/7823930

    Article  Google Scholar 

  83. 83.

    Piotrowski AP, Napiorkowski JJ, Piotrowska AE (2020) Population size in particle swarm optimization. Swarm Evol Comput 58:100718

    Google Scholar 

  84. 84.

    Ding J, Wang Q, Zhang Q, Ye Q, Ma Y (2019) A hybrid particle swarm optimization-Cuckoo Search Algorithm and its engineering applications. Math Probl Eng 2019:1–12. https://doi.org/10.1155/2019/5213759

    Article  MATH  Google Scholar 

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Funding

The research works are done using the facilities at Universiti Malaysia Pahang for Research University Grant (RDU1803144) and Ministry of Higher Education for Fundamental Research Grant Scheme (FRGS) (FRGS/1/2019/TK10/UMP/03/2) (RDU1901193), through the course of this research.

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Nor Atiqah Zolpakar: writing – original draft, analysis of technical papers. Mohd Fuad Yasak: Conceptualization, writing – review and editing. Sunil Pathak: Critical review on advanced machining, writing – review and editing.

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Correspondence to Nor Atiqah Zolpakar.

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Zolpakar, N.A., Yasak, M.F. & Pathak, S. A review: use of evolutionary algorithm for optimisation of machining parameters. Int J Adv Manuf Technol 115, 31–47 (2021). https://doi.org/10.1007/s00170-021-07155-7

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Keywords

  • Machining process
  • Machining parameter
  • Optimisation
  • Evolutionary algorithm