Advertisement

Modeling and multi-objective optimization for minimizing surface roughness, cutting force, and power, and maximizing productivity for tempered stainless steel AISI 420 in turning operations

  • Abderrahmen Zerti
  • Mohamed Athmane Yallese
  • Ikhlas Meddour
  • Salim Belhadi
  • Abdelkrim Haddad
  • Tarek Mabrouki
ORIGINAL ARTICLE
  • 42 Downloads

Abstract

The present study aims at investigating the influence of the different machining parameters represented by the cutting speed (Vc), the depth of cut (ap), and the feed rate (f) on the output performance parameters expressed through the surface roughness, the cutting force and power, and the material removal rate (i.e., Ra, Fz, Pc, and MRR) during dry hard turning operation of martensitic stainless steel (AISI 420) treated at 59HRC. The machining tests were carried out using the coated mixed ceramic insert (CC6050) according to the Taguchi design (L25). The analysis of the variance (ANOVA) and the Pareto chart analysis led to quantifying the influence of the (Vc, ap, and f) on the output parameters. The response surface methodology (RSM) and the artificial neural networks (ANN) approaches were applied and compared for output parameters modeling. Attempt was further made to optimize the machining parameters using the desirability function (DF). Four objectives were considered including the maximization of the quality and the productivity (through minimizing Ra and maximizing MRR), and reducing the energy consumption over minimizing both (Fz) and (Pc). The results indicated that (Ra) is strongly influenced by the feed rate (in the order of 80.71%), while the depth of cut seems to be the property having the most influence on the cutting force (65.31%), the cutting power (37.56%), and the material removal rate (36.45%). Furthermore, ANN and RSM models were found to predict well experimental results with the former showing higher accuracy. The machining of AISI 420 (59 HRC) steel with coated ceramic led to achieving a quality surface comparable to that found in grinding (i.e., Ra < 0.4 μm).

Keywords

Hard turning Modeling Response surface methodology Artificial neural networks Surface roughness Cutting force Martensitic stainless steel Optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sourmail T, Bhadeshia HKDH (2011) Stainless steels, University of CambridgeGoogle Scholar
  2. 2.
    El-Tamimi AM et al (2010) Developed models for understanding and predicting the machinability of a hardened martensitic stainless steel. Mater Manuf Process 25:758–768CrossRefGoogle Scholar
  3. 3.
    Byrne G, Dornfeld D, Denkena B (2003) Advancing cutting technology. CIRP Ann Manuf Technol 52:483–507CrossRefGoogle Scholar
  4. 4.
    Sales WF, Costa LA, Santos SC, Diniz AE, Bonney J, Ezugwu EO (2009) Performance of coated, cemented carbide, mixed-ceramic and PCBN-H tools when turning W320 steel. Int J Adv Manuf Technol 41:660–669CrossRefGoogle Scholar
  5. 5.
    Stephenson DA, Agapiou JS (2006) Metal cutting theory and practice. Taylor and Francis Group, Boca Raton, pp 17–70Google Scholar
  6. 6.
    Noordin MY, Kurniawan D, Sharif S (2007) Hard turning of stainless steel using wiper coated carbide tool. International Journal of Precision Technology (1):75–84CrossRefGoogle Scholar
  7. 7.
    Sobiyi K, Sigalas I, Akdogan G, Turan Y (2015) Performance of mixed ceramics and CBN tools during hard turning of martensitic stainless steel. Int J Adv Manuf Technol 77:861–871CrossRefGoogle Scholar
  8. 8.
    Sobiyi, Kehinde et Sigalas, Iakovos. Optimisation in Hard Turning of Martensitic Stainless Steel using Taguchi Method. International conference on chemical, Civil and Environmental Engineering (ICCCEE’2015) 2015; 111–115Google Scholar
  9. 9.
    Lima JG, Avila RF, Abrao AM, Faustino M, Davim JP (2005) Hard turning: AISI 4340 high strength low alloy steel and AISI D2 cold work tool steel. J Mater Process Technol 169:388–395CrossRefGoogle Scholar
  10. 10.
    Sahin Y (2009) Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels. J Mater Process Technol 209:3478–3489CrossRefGoogle Scholar
  11. 11.
    Chou YK, Evans CJ, Barash MM (2002) Experimental investigation on CBN turning of hardened AISI 52100 steel. J Mater Process Technol 124:274–283CrossRefGoogle Scholar
  12. 12.
    Günay M, Yücel E (2013) Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron. Measurement 46:913–919CrossRefGoogle Scholar
  13. 13.
    Stru, Multi Namenska Optimizacija, Enja Z. Uporabo, and T. M. N. G. Podlagi. Multi-objective optimization of the cutting forces in turning operations using the grey-based Taguchi method. Materiali in tehnologije 2011; 45: 105–110Google Scholar
  14. 14.
    Shahrom MS, Yahya NM, Yusoff AR (2013) Taguchi method approach on effect of lubrication condition on surface roughness in milling operation. Procedia Engineering 53:594–599CrossRefGoogle Scholar
  15. 15.
    Aouici H, Bouchelaghem H, Yallese MA, Elbah M, Fnides B (2014) Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology. Int J Adv Manuf Technol 73(9–12):1775–1788CrossRefGoogle Scholar
  16. 16.
    Bouzid L, Yallese MA, Chaoui K, Mabrouki T, Boulanouar L (2015) Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology. Proc Inst Mech Eng B J Eng Manuf 229:45–61CrossRefGoogle Scholar
  17. 17.
    Noordin MY, Venkatesh VC, Sharif S (2007) Dry turning of tempered martensitic stainless tool steel using coated cermet and coated carbide tools. J Mater Process Technol 185:83–90CrossRefGoogle Scholar
  18. 18.
    Axinte DA, Dewes RC (2002) Surface integrity of hot work tool steel after high speed milling-experimental data and empirical models. J Mater Process Technol 127:325–335CrossRefGoogle Scholar
  19. 19.
    Palanisamy D, Senthil P (2016) Optimization on turning parameters of 15-5PH stainless steel using Taguchi based grey approach and Topsis. Archive of Mechanical Engineering (63):397–412CrossRefGoogle Scholar
  20. 20.
    Zerti O, Yallese M, Zerti A, Belhadi S, Girardin F (2018) Simultaneous improvement of surface quality and productivity using grey relational analysis based Taguchi design for turning couple (AISI D3 steel/mixed ceramic tool (Al2O3+ TiC)). Int J Ind Eng Comput 9:173–119Google Scholar
  21. 21.
    Bouzid L, Boutabba S, Yallese MA, Belhadi S, Girardin F (2014) Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel. Int J Adv Manuf Technol 74:879–891CrossRefGoogle Scholar
  22. 22.
    Bagaber SA, Yusoff AR (2017) Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. J Clean Prod 157:30–46CrossRefGoogle Scholar
  23. 23.
    Davim J, Paulo V, Gaitonde N, Karnik SR (2008) Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J Mater Process Technol 205(1–3):16–23CrossRefGoogle Scholar
  24. 24.
    Anand G, Alagumurthi N, Elansezhian R, Palanikumar K, Venkateshwaran N (2018) Investigation of drilling parameters on hybrid polymer composites using grey relational analysis, regression, fuzzy logic, and ANN models. J Braz Soc Mech Sci Eng 40(4):214CrossRefGoogle Scholar
  25. 25.
    Karabulut Ş (2015) Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement 66:139–149CrossRefGoogle Scholar
  26. 26.
    Camposeco-Negrete C (2013) Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J Clean Prod 53:195–203CrossRefGoogle Scholar
  27. 27.
    Asiltürk I, Akkuş H (2011) Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 44:1697–1704Google Scholar
  28. 28.
    Zerti O, Yallese MA, Khettabi R, Chaoui K, Mabrouki T (2017) Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method. Int J Adv Manuf Technol 89:1915–1934CrossRefGoogle Scholar
  29. 29.
    Bouzid L, Yallese MA, Belhadi S, Mabrouki T, Boulanouar L (2014) RMS-based optimization of surface roughness when turning AISI 420 stainless steel. Int J Mater Prod Technol 49:224–251CrossRefGoogle Scholar
  30. 30.
    Chabbi A, Yallese MA, Nouioua M, Meddour I, Mabrouki T, Girardin F (2017) Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods. Int J Adv Manuf Technol 91:2267–2290CrossRefGoogle Scholar
  31. 31.
    Bezerra MA et al (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76(5):965–977MathSciNetCrossRefGoogle Scholar
  32. 32.
    Korkmaz ME, Günay M (2018) Finite element modelling of cutting forces and power consumption in turning of AISI 420 martensitic stainless steel. Arab J Sci Eng:1–8Google Scholar
  33. 33.
    Berkani S, Yallese M, Boulanouar L, Mabrouki T (2015) Statistical analysis of AISI304 austenitic stainless steel machining using Ti (C, N)/Al2O3/TiN CVD coated carbide tool. Int J Ind Eng Comput 6(4):539–552Google Scholar
  34. 34.
    Lalwani DI, Mehta NK, Jain PK (2008) Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. J Mater Process Technol 206:167–179CrossRefGoogle Scholar
  35. 35.
    Tebassi H, Yallese MA, Meddour I, Girardin F, Mabrouki T (2017) On the modeling of surface roughness and cutting force when turning of Inconel 718 using artificial neural network and response surface methodology: accuracy and benefit. Periodica Polytechnica Engineering Mechanical Engineering 61(1):1–11CrossRefGoogle Scholar
  36. 36.
    Nouioua M, Yallese MA, Khettabi R, Belhadi S, Bouhalais ML, Girardin F (2017) Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN). Int J Adv Manuf Technol 93:2485–2504CrossRefGoogle Scholar
  37. 37.
    Samanta B, Erevelles W, Omurtag Y (2008) Prediction of workpiece surface roughness using soft computing. Proc Inst Mech Eng B J Eng Manuf 222:1221–1232CrossRefGoogle Scholar
  38. 38.
    Khellaf A, Aouici H, Smaiah S, Boutabba S, Yallese MA, Elbah M (2017) Comparative assessment of two ceramic cutting tools on surface roughness in hard turning of AISI H11 steel: including 2D and 3D surface topography. Int J Adv Manuf Technol 89(1–4):333–354CrossRefGoogle Scholar
  39. 39.
    Żak K, Grzesik W (2017) Metrological aspects of surface topographies produced by different machining operations regarding their potential functionality. Metrology and Measurement Systems 24(2):325–335CrossRefGoogle Scholar
  40. 40.
    Grzesik W (2018) Prediction of surface topography in precision hard machining based on modelling of the generation mechanisms resulting from a variable feed rate. Int J Adv Manuf Technol 94(9–12):4115–4123CrossRefGoogle Scholar
  41. 41.
    Bensouilah H, Aouici H, Meddour I, Yallese MA, Mabrouki T, Girardin F (2016) Performance of coated and uncoated mixed ceramic tools in hard turning process. Measurement 82:1–18CrossRefGoogle Scholar
  42. 42.
    Hessainia Z, Yallese MA, Bouzid L, Mabrouki T (2015) On the application of response surface methodology for predicting and optimizing surface roughness and cutting forces in hard turning by PVD coated insert. Int J Ind Eng Comput 6:267–284Google Scholar
  43. 43.
    Belhadi S, Kaddeche M, Chaoui K, Yallese MA (2016) Machining optimization of HDPE pipe using the Taguchi method and Grey relational analysis. Int Polym Process 31(4):491–502CrossRefGoogle Scholar
  44. 44.
    Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimisation in integrated process planning and scheduling. Robot Comput Integr Manuf 25(2):280–288CrossRefGoogle Scholar
  45. 45.
    Qu S, Zhao J, Wang T (2017) Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. Int J Adv Manuf Technol 89(5–8):2399–2409CrossRefGoogle Scholar
  46. 46.
    Selaimia AA, Yallese MA, Bensouilah H, Meddour I, Khattabi R, Mabrouki T (2017) Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach. Measurement 107:53–67CrossRefGoogle Scholar
  47. 47.
    Shahrajabian H, Farahnakian M (2013) Modeling and multi-constrained optimization in drilling process of carbon fiber reinforced epoxy composite. Int J Precis Eng Manuf 14:1829–1837CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mechanics and Structures Research Laboratory (LMS), Mechanical Engineering DepartmentUniversité 8 Mai 1945 GuelmaGuelmaAlgeria
  2. 2.École Nationale Supérieure de Technologie (ENST)AlgiersAlgeria
  3. 3.Laboratoire de Mécanique Appliquée des Nouveaux Matériaux (LMANM)Université 8 Mai 1945 GuelmaGuelmaAlgeria
  4. 4.ENITUniversity of Tunis El ManarTunisTunisia

Personalised recommendations