Skip to main content
SpringerLink
Log in

Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study investigated the AISI 1040 steel turning in dry environment with four cutting inserts of different corner radii coated by CVD method. Experimental investigations were performed for different levels of cutting speeds, feeds and depths of cut using a randomized full factorial design. Quality characteristics of the workpiece machined surface were measured (arithmetical mean roughness) as well as the cutting inserts tool life characteristics (average width of flank wear). Machining times and chip volume were calculated, and based on this, chip quantity in time (material removal rate). The response surface approach and analysis of variance were used to determine the effects of input process parameters on the response variables. Based on the derived regression models, multi-objective optimization of output process parameters was performed using genetic algorithm. The objective function was simultaneous minimization of flank wear, minimization of surface roughness and maximization of material removal rate. The parameters of the genetic algorithm (crossover ratio, crossover fraction, mutation rate, Pareto front population fraction) were varied to obtain the optimal values of the objective function. Additionally, a sensitivity analysis was performed, which showed that the selected values of genetic algorithm parameters gave the best (minimum) value of objective function. Instead of the usual approach of obtaining only one combination of optimal parameters as a final solution, the basic idea was to obtain multiple combinations of optimal input process parameters depending on the importance of each output process parameter, i.e. requirements of production. Accordingly, the results of multi-objective optimization showed that there are a large number of Pareto optimal solutions. To validate the optimal input and output process values, confirmation experiments were conducted for selected trials of Pareto optimal results obtained from multi-objective optimization. A mean error percentage of 1.478% and 1.146% for flank wear and arithmetical mean roughness, respectively, proves that the predicted optimum values are confirmed by experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Gok A (2015) A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement 70:100–109. https://doi.org/10.1016/j.measurement.2015.03.037

    Article  Google Scholar 

  2. Dureja J, Gupta V, Sharma VS, Dogra M, Bhatti MS (2016) A review of empirical modeling techniques to optimize machining parameters for hard turning applications. Proc Inst Mech Eng B J Eng Manuf 230:389–404. https://doi.org/10.1177/0954405414558731

    Article  Google Scholar 

  3. Singh R, Dureja JS, Dogra M, Randhawa JS (2019) Optimization of machining parameters under MQL turning of Ti–6Al–4V alloy with textured tool using multi-attribute decision-making methods. World J Eng 16:648–659. https://doi.org/10.1108/WJE-06-2019-0170

    Article  Google Scholar 

  4. Nguyen TT (2020) An energy-efficient optimization of the hard turning using rotary tool. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05149-2

    Article  Google Scholar 

  5. Tuffy K, Byrne G, Dowling D (2004) Determination of the optimum TiN coating thickness on WC inserts for machining carbon steels. J Mater Process Technol 155–156:1861–1866. https://doi.org/10.1016/j.jmatprotec.2004.04.277

    Article  Google Scholar 

  6. Gunay M, Seker U, Sur G (2006) Design and construction of a dynamometer to evaluate the influence of cutting tool rake angle on cutting forces. Mater Des 27:1097–1101. https://doi.org/10.1016/j.matdes.2005.04.003

    Article  Google Scholar 

  7. Yaldiz S, Unsacar F, Saglam H (2006) Comparison of experimental results obtained by designed dynamometer to fuzzy model for predicting cutting forces in turning. Mater Des 27(10):1139–1147. https://doi.org/10.1016/j.matdes.2005.03.010

    Article  Google Scholar 

  8. Saglam H, Unsacar F, Yaldiz S (2006) Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. Int J Mach Tools Manuf 46:132–141. https://doi.org/10.1016/j.ijmachtools.2005.05.002

    Article  Google Scholar 

  9. Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47:2140–2152. https://doi.org/10.1016/j.ijmachtools.2007.04.013

    Article  Google Scholar 

  10. Asilturk I, Cunkas M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826–5832. https://doi.org/10.1016/j.eswa.2010.11.041

    Article  Google Scholar 

  11. Neseli S, Yaldiz S, Turkes E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44(3):580–587. https://doi.org/10.1016/j.measurement.2010.11.018

    Article  Google Scholar 

  12. Topal ES, Cogun C (2011) Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars. J Intell Manuf 22:853–865. https://doi.org/10.1007/s10845-009-0360-0

    Article  Google Scholar 

  13. Cohen G, Gilles P, Segonds S, Mousseigne M, Lagarrigue P (2012) Thermal and mechanical modeling during dry turning operations. Int J Adv Manuf Technol 58:133–140. https://doi.org/10.1007/s00170-011-3372-9

    Article  Google Scholar 

  14. Asilturk I (2012) Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression. Int J Adv Manuf Technol 63:249–257. https://doi.org/10.1007/s00170-012-3903-z

    Article  Google Scholar 

  15. Venkata Rao K, Murthy BSN, Mohan Rao N (2013) Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring. Measurement 46:4075–4084. https://doi.org/10.1016/j.measurement.2013.07.021

    Article  Google Scholar 

  16. Venkata Rao K, Murthy B, Mohan Rao N (2015) Experimental study on surface roughness and vibration of workpiece in boring of AISI 1040 steels. Proc Inst Mech Eng B J Eng Manuf 229:703–712. https://doi.org/10.1177/0954405414531247

    Article  Google Scholar 

  17. Prasad BS, Babu MP, Reddy YR (2016) Evaluation of correlation between vibration signal features and three-dimensional finite element simulations to predict cutting tool wear in turning operation. Proc Inst Mech Eng B J Eng Manuf 230:203–214. https://doi.org/10.1177/0954405414554018

    Article  Google Scholar 

  18. Venkata Rao K, Vidhu KP, Kumar TA, Rao NN, Murthy PBGSN, Balaji M (2016) An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels. Int J Adv Manuf Technol 83:919–927. https://doi.org/10.1007/s00170-015-7621-1

    Article  Google Scholar 

  19. Yadav RN (2017) A hybrid approach of Taguchi-response surface methodology for modeling and optimization of duplex turning process. Measurement 100:131–138. https://doi.org/10.1016/j.measurement.2016.12.060

    Article  Google Scholar 

  20. Haque T, Kumar S, Upadhaya D, Barman M, Mukhopadhyay A (2017) Optimization of multiple roughness characteristics for turning of AISI 1040 steel under different cutting conditions. Int J Eng Technol 10:1–10. https://doi.org/10.18052/www.scipress.com/ijet.10.1

    Article  Google Scholar 

  21. Akkus H (2018) Optimising the effect of cutting parameters on the average surface roughness in a turning process with the Taguchi method. Mater Tehnol 52:781–785. https://doi.org/10.17222/mit.2018.110

    Article  Google Scholar 

  22. Jhodkar D, Amarnath M, Chelladurai H, Ramkumar J (2018) Performance assessment of microwave treated WC insert while turning AISI 1040 steel. J Mech Sci Technol 32:2551–2558. https://doi.org/10.1007/s12206-018-0512-2

    Article  Google Scholar 

  23. Jhodkar D, Amarnath M, Chelladurai H, Ramkumar J (2018) Experimental investigations to enhance the machining performance of tungsten carbide tool insert using microwave treatment process. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-018-1096-6

    Article  Google Scholar 

  24. Dhar NR, Paul S, Chattopadhyay AB (2002) The influence of cryogenic cooling on tool wear, dimensional accuracy and surface finish in turning AISI 1040 and E4340C steels. Wear 249:932–942. https://doi.org/10.1016/s0043-1648(01)00825-0

    Article  Google Scholar 

  25. Dhar NR, Ahmed MT, Islam S (2007) An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel. Int J Mach Tools Manuf 47:748–753. https://doi.org/10.1016/j.ijmachtools.2006.09.017

    Article  Google Scholar 

  26. Vamsi Krishna P, Rao DN, Srikant RR (2009) Predictive modelling of surface roughness and tool wear in solid lubricant assisted turning of AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 223:929–934. https://doi.org/10.1243/13506501jet475

    Article  Google Scholar 

  27. Ramana SV, Ramji K, Satyanarayana B (2010) Studies on the behaviour of the green particulate fluid lubricant in its nano regime when machining AISI 1040 steel. Proc Inst Mech Eng B J Eng Manuf 224:1491–1501. https://doi.org/10.1243/09544054jem1862

    Article  Google Scholar 

  28. Vamsi Krishna P, Srikant RR, Nageswara Rao D (2010) Experimental investigation on the performance of nanoboric acid suspensions in SAE-40 and coconut oil during turning of AISI 1040 steel. Int J Mach Tools Manuf 50:911–916. https://doi.org/10.1016/j.ijmachtools.2010.06.001

    Article  Google Scholar 

  29. Amrita M, Srikant R, Sitaramaraju A, Prasad M, Krishna PV (2013) Experimental investigations on influence of mist cooling using nanofluids on machining parameters in turning AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 227:1334–1346. https://doi.org/10.1177/1350650113491934

    Article  Google Scholar 

  30. Srikiran S, Ramji K, Satyanarayana B, Ramana S (2014) Investigation on turning of AISI 1040 steel with the application of nano-crystalline graphite powder as lubricant. Proc Inst Mech Eng C J Mech Eng Sci 228:1570–1580. https://doi.org/10.1177/0954406213509612

    Article  Google Scholar 

  31. Gupta MK, Singh G, Sood PK (2015) Experimental investigation of machining AISI 1040 medium carbon steel under cryogenic machining: a comparison with dry machining. J Inst Eng India Ser C 96:373–379. https://doi.org/10.1007/s40032-015-0178-9

    Article  Google Scholar 

  32. Padmini R, Krishna PV, Mohana Rao GK (2016) Experimental evaluation of nano-molybdenum disulphide and nano-boric acid suspensions in vegetable oils as prospective cutting fluids during turning of AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 230:493–505. https://doi.org/10.1177/1350650115601694

    Article  Google Scholar 

  33. Ajay Vardhaman BS, Amarnath M, Jhodkar D, Ramkumar J, Chelladurai H, Roy MK (2018) Influence of coconut oil on tribological behavior of carbide cutting tool insert during turning operation. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-018-1379-y

    Article  Google Scholar 

  34. Mia M, Dhar NR (2019) Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Comput Appl 31:2349–2370. https://doi.org/10.1007/s00521-017-3192-4

    Article  Google Scholar 

  35. Usha M, Rao GS (2020) Optimization of multiple objectives by genetic algorithm for turning of AISI 1040 steel using Al2O3 nano fluid with MQL. Trib Ind 42:70–80. https://doi.org/10.24874/ti.2020.42.01.07

    Article  Google Scholar 

  36. Sahinoglu A, Rafighi M (2020) Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel. Mater Test 62:85–95. https://doi.org/10.3139/120.111458

    Article  Google Scholar 

  37. Gugulothu S, Pasa VK (2020) Experimental investigation to study the performance of CNT/MoS2 hybrid nanofluid in turning of AISI 1040 steel. Aust J Mech Eng. https://doi.org/10.1080/14484846.2020.1756067

    Article  Google Scholar 

  38. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88. https://doi.org/10.1016/j.ins.2012.03.005

    Article  Google Scholar 

  39. Ahilan C, Kumanan S, Sivakumaran N, Edwin Raja Dhas J (2013) Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Appl Soft Comput 13:1543–1551. https://doi.org/10.1016/j.asoc.2012.03.071

    Article  Google Scholar 

  40. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46:445–464. https://doi.org/10.1007/s00170-009-2104-x

    Article  Google Scholar 

  41. Garg A, Bhalerao Y, Tai K (2013) Review of empirical modelling techniques for modelling of turning process. Int J Model Identif Control 20:121–129. https://doi.org/10.1504/ijmic.2013.056184

    Article  Google Scholar 

  42. Sibalija TV (2019) Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018). Appl Soft Comput 84:105743. https://doi.org/10.1016/j.asoc.2019.105743

    Article  Google Scholar 

  43. Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007–2011). Expert Syst Appl 39:9909–9927. https://doi.org/10.1016/j.eswa.2012.02.109

    Article  Google Scholar 

  44. Leo Kumar SP (2017) State of the art-intense review on artificial intelligence systems application process i planning and manufacturing. Eng Appl Artif Intell 65:294–329. https://doi.org/10.1016/j.engappai.2017.08.005

    Article  Google Scholar 

  45. Sterpin Valic G, Cukor G, Jurkovic Z, Brezocnik M (2019) Multi-criteria optimization of turning of martensitic stainless steel for sustainability. Int J Simul Model 18:632–642. https://doi.org/10.2507/IJSIMM18(4)495

    Article  Google Scholar 

  46. Ghosh T, Martinsen K (2020) Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms. Eng Sci Technol Int J 23:650–663. https://doi.org/10.1016/j.jestch.2019.09.003

    Article  Google Scholar 

  47. Chavez-Garcia H, Castillo-Villar KK (2018) Simulation-based model for the optimization of machining parameters in a metal-cutting operation. Simul Model Pract Theory 84:204–221. https://doi.org/10.1016/j.simpat.2018.02.008

    Article  Google Scholar 

  48. Weichert D, Link P, Stoll A, Ruping S, Ihlenfeldt S, Wrobel S (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104:1889–1902. https://doi.org/10.1007/s00170-019-03988-5

    Article  Google Scholar 

  49. Rana N, Latiff MSA, Abdulhamid SM, Chiroma H (2020) Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Comput Appl 32:16245–16277. https://doi.org/10.1007/s00521-020-04849-z

    Article  Google Scholar 

  50. Srinivasan S, Ramakrishnan S (2011) Evolutionary multi objective optimization for rule mining: a review. Artif Intell Rev 36:205–248. https://doi.org/10.1007/s10462-011-9212-3

    Article  Google Scholar 

  51. Ojha M, Singh KP, Chakraborty P, Verma S (2019) A review of multi-objective optimisation and decision making using evolutionary algorithms. Int J Bio Inspir Com 14:69. https://doi.org/10.1504/ijbic.2019.101640

    Article  Google Scholar 

  52. Liu Q, Li X, Liu H, Guo Z (2020) Multi-objective metaheuristics for discrete optimization problems: a review of the state-of-the-art. Appl Soft Comput 93:106382. https://doi.org/10.1016/j.asoc.2020.106382

    Article  Google Scholar 

  53. Gullu H (2017) A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming. Neural Comput Appl 28:407–420. https://doi.org/10.1007/s00521-016-2360-2

    Article  Google Scholar 

  54. Quiza Sardinas R, Rivas Santana M, Alfonso Brindis E (2006) Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Eng Appl Artif Intell 19:127–133. https://doi.org/10.1016/j.engappai.2005.06.007

    Article  Google Scholar 

  55. D’Addona DM, Teti R (2013) Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP 7:323–328. https://doi.org/10.1016/j.procir.2013.05.055

    Article  Google Scholar 

  56. Lv J, Zhao JB, Liu QG (2013) Optimization of cutting parameters based on multi-objective genetic algorithm NSGA- II. Appl Mech Mater 281:517–522. https://doi.org/10.4028/www.scientific.net/amm.281.517

    Article  Google Scholar 

  57. Klancnik S, Hrelja M, Balic J, Brezocnik M (2016) Multi-objective optimization of the turning process using a gravitational search algorithm (GSA) and NSGA-II approach. Adv Prod Eng Manag 11:366–376. https://doi.org/10.14743/apem2016.4.234

    Article  Google Scholar 

  58. Manav O, Chinchanikar S (2018) Multi-objective optimization of hard turning: a genetic algorithm approach. Mater Today 5:12240–12248. https://doi.org/10.1016/j.matpr.2018.02.201

    Article  Google Scholar 

  59. 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 5:6897–6905. https://doi.org/10.1016/j.matpr.2017.11.351

    Article  Google Scholar 

  60. Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18:135–140. https://doi.org/10.1007/s00521-007-0166-y

    Article  Google Scholar 

  61. Jasiewicz M, Miadlicki K (2020) An integrated CNC system for chatter suppression in turning. Adv Prod Eng Manag 15:318–330. https://doi.org/10.14743/apem2020.3.368

    Article  Google Scholar 

  62. Yang MS, Ba L, Xu EB, Li Y, Gao XQ, Liu Y, Li Y (2019) Batch optimization in integrated scheduling of machining and assembly. Int J Simul Model 18:689–698. https://doi.org/10.2507/IJSIMM18(4)CO17

    Article  Google Scholar 

  63. Tschatsch H (2009) Applied machining technology. Springer, Berlin. https://doi.org/10.1007/978-3-642-01007-1

    Book  Google Scholar 

  64. Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    MATH  Google Scholar 

Download references

Acknowledgements

The results presented in this paper are obtained in the framework of the Project No. SV001 entitled “Modelling and optimizing processes applicable in maintenance” funded by the University of Slavonski Brod, Mechanical Engineering Faculty in Slavonski Brod, Republic of Croatia, and within the Project No. 451-03-68/2020-14/200156 entitled “Innovative scientific and artistic research from the FTS (activity) domain” funded by the Ministry of Education, Science and Technological Development of Republic of Serbia.

Author information

Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000, Novi Sad, Serbia

    Djordje Vukelic & Zeljko Kanovic

  2. Mechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, 35000, Slavonski Brod, Croatia

    Katica Simunovic, Tomislav Saric & Goran Simunovic

  3. Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000, Kragujevac, Serbia

    Branko Tadic

Authors
  1. Djordje Vukelic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katica Simunovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeljko Kanovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomislav Saric
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Branko Tadic
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Goran Simunovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Goran Simunovic.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 15.

Table 15 Optimal combinations of input process parameters for different optimal combinations of output process parameters
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vukelic, D., Simunovic, K., Kanovic, Z. et al. Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm. Neural Comput & Applic 33, 12445–12475 (2021). https://doi.org/10.1007/s00521-021-05877-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05877-z

Keywords

Search

Navigation