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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

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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).

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References

  1. Sourmail T, Bhadeshia HKDH (2011) Stainless steels, University of Cambridge

  2. El-Tamimi AM et al (2010) Developed models for understanding and predicting the machinability of a hardened martensitic stainless steel. Mater Manuf Process 25:758–768

    Article  Google Scholar 

  3. Byrne G, Dornfeld D, Denkena B (2003) Advancing cutting technology. CIRP Ann Manuf Technol 52:483–507

    Article  Google Scholar 

  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 41:660–669

    Article  Google Scholar 

  5. Stephenson DA, Agapiou JS (2006) Metal cutting theory and practice. Taylor and Francis Group, Boca Raton, pp 17–70

    Google Scholar 

  6. Noordin MY, Kurniawan D, Sharif S (2007) Hard turning of stainless steel using wiper coated carbide tool. International Journal of Precision Technology (1):75–84

    Article  Google Scholar 

  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 77:861–871

    Article  Google Scholar 

  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; 111–115

  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 169:388–395

    Article  Google Scholar 

  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 209:3478–3489

    Article  Google Scholar 

  11. Chou YK, Evans CJ, Barash MM (2002) Experimental investigation on CBN turning of hardened AISI 52100 steel. J Mater Process Technol 124:274–283

    Article  Google Scholar 

  12. Günay M, Yücel E (2013) Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron. Measurement 46:913–919

    Article  Google Scholar 

  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: 105–110

  14. Shahrom MS, Yahya NM, Yusoff AR (2013) Taguchi method approach on effect of lubrication condition on surface roughness in milling operation. Procedia Engineering 53:594–599

    Article  Google Scholar 

  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):1775–1788

    Article  Google Scholar 

  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 229:45–61

    Article  Google Scholar 

  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 185:83–90

    Article  Google Scholar 

  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 127:325–335

    Article  Google Scholar 

  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):397–412

    Article  Google Scholar 

  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 9:173–119

    Google Scholar 

  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 74:879–891

    Article  Google Scholar 

  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 157:30–46

    Article  Google Scholar 

  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):16–23

    Article  Google Scholar 

  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):214

    Article  Google Scholar 

  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 66:139–149

    Article  Google Scholar 

  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 53:195–203

    Article  Google Scholar 

  27. Asiltürk I, Akkuş H (2011) Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 44:1697–1704

    Google Scholar 

  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 89:1915–1934

    Article  Google Scholar 

  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 49:224–251

    Article  Google Scholar 

  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 91:2267–2290

    Article  Google Scholar 

  31. Bezerra MA et al (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76(5):965–977

    Article  MathSciNet  Google Scholar 

  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:1–8

  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):539–552

    Google Scholar 

  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 206:167–179

    Article  Google Scholar 

  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):1–11

    Article  Google Scholar 

  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 93:2485–2504

    Article  Google Scholar 

  37. Samanta B, Erevelles W, Omurtag Y (2008) Prediction of workpiece surface roughness using soft computing. Proc Inst Mech Eng B J Eng Manuf 222:1221–1232

    Article  Google Scholar 

  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):333–354

    Article  Google Scholar 

  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):325–335

    Article  Google Scholar 

  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):4115–4123

    Article  Google Scholar 

  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 82:1–18

    Article  Google Scholar 

  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 6:267–284

    Google Scholar 

  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):491–502

    Article  Google Scholar 

  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):280–288

    Article  MATH  Google Scholar 

  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):2399–2409

    Article  Google Scholar 

  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 107:53–67

    Article  Google Scholar 

  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 14:1829–1837

    Article  Google Scholar 

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Authors and Affiliations

  1. Mechanics and Structures Research Laboratory (LMS), Mechanical Engineering Department, Université 8 Mai 1945 Guelma, BP401, 24000, Guelma, Algeria

    Abderrahmen Zerti, Mohamed Athmane Yallese, Ikhlas Meddour & Salim Belhadi

  2. École Nationale Supérieure de Technologie (ENST), Algiers, Algeria

    Ikhlas Meddour

  3. Laboratoire de Mécanique Appliquée des Nouveaux Matériaux (LMANM), Université 8 Mai 1945 Guelma, BP401, 24000, Guelma, Algeria

    Abdelkrim Haddad

  4. ENIT, University of Tunis El Manar, Tunis, Tunisia

    Tarek Mabrouki

Authors
  1. Abderrahmen Zerti
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  2. Mohamed Athmane Yallese
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  3. Ikhlas Meddour
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  4. Salim Belhadi
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  5. Abdelkrim Haddad
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  6. Tarek Mabrouki
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Correspondence to Abderrahmen Zerti.

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Zerti, A., Yallese, M.A., Meddour, I. et al. 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. Int J Adv Manuf Technol 102, 135–157 (2019). https://doi.org/10.1007/s00170-018-2984-8

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  • DOI: https://doi.org/10.1007/s00170-018-2984-8

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