Production Engineering

, Volume 4, Issue 2–3, pp 115–125 | Cite as

Empirical modeling of hard turning of AISI 6150 steel using design and analysis of computer experiments

  • Benedikt SiebenEmail author
  • Tobias Wagner
  • Dirk Biermann
Production Process


In the present paper an experimental study to investigate the turning of hardened AISI 6150 heat treatable steel using polycrystalline boron nitride (PCBN) tools is presented. Design and analysis of computer experiments (DACE) was used to generate a comprehensive empirical description of the process characteristics. More specific, the effects of the parameters cutting speed, feed and depth of cut on the objectives tool wear, tool life, tool life volume, surface finish and process forces were modeled. A total of 157 experiments was carried out with 15 different parameter-value sets to obtain the training data for modeling the progression of the objectives versus cutting path length and width of flank wear land. Pseudo-3D surface plots are generated to visualize the effects and interactions. Unexpected effects of depth of cut on tool life were found and the validity of conclusions about the effect of cutting speed on tool wear and tool life are discussed. Moreover, qualitative explanations for some of the observed effects are presented.


Hard turning Design and analysis of computer experiments Empirical modeling Tool wear Surface finish 



This paper is based on investigations of the collaborative research center SFB/TR TRR 30, which is kindly supported by the DFG.


  1. 1.
    Bhattacharya A, Das S, Majumder P, Batish A (2009) Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of aisi 1045 steel using taguchi design and anova. Prod Eng Res Dev 3:31–40CrossRefGoogle Scholar
  2. 2.
    Biermann D, Weinert K, Wagner T (2008a) Model-based optimization revisited: towards real-world processes. In: Michalewicz Z, Reynolds RG (eds) 2008 IEEE congress on evolutionary computation (CEC 2008), Hong Kong, pp 3514–3521Google Scholar
  3. 3.
    Biermann D, Zabel A, Gruenert S, Sieben B, Wagner T (2008b) Machining of functional graded workpieces. In: Byrne G, O’Donnel G (eds) Proceedings of the 3rd CIRP international conference high performance cutting (HPC), Dublin, Ireland, pp 723–732Google Scholar
  4. 4.
    Caydas U, Hascalik A (2008) A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582CrossRefGoogle Scholar
  5. 5.
    Chou YK, Evans CJ (1997) Tool wear mechanism in continuous cutting of hardened tool steels. Wear 212:59–65CrossRefGoogle Scholar
  6. 6.
    Chou YK, Evans CJ (1999) Cubic boron nitride tool wear in interrupted hard cutting. Wear 225–299(Part 1):234–245CrossRefGoogle Scholar
  7. 7.
    Choudhury I, El-Baradie M (1999) Machinability assessment of inconel 718 by factorial design of experiment coupled with response surface methodology. J Mater Process Technol 95(1–3):30–39CrossRefGoogle Scholar
  8. 8.
    Costes J, Guillet Y, Poulachon G, Dessoly M (2007) Tool-life and wear mechanisms of cbn tools in machining of inconel 718. Int J Mach Tools Manuf 47:1081–1087CrossRefGoogle Scholar
  9. 9.
    Davim JP, Gaitonde V, Karnik S (2008) Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ann models. J Mater Process Technol 205:16–23CrossRefGoogle Scholar
  10. 10.
    Denkena B, Boehnke D, Meyer R (2008) Reduction of wear induced surface zone effects during hard turning by means of new tool geometries. Prod Eng Res Dev 2:123–132CrossRefGoogle Scholar
  11. 11.
    Diniz AE, de Oliveira AJ (2008) Hard turning of interrupted surface using cbn tools. J Mater Process Technol 195:275–281CrossRefGoogle Scholar
  12. 12.
    Feng CX (2001) An experimental study of the impact of turning parameters on surface roughness. In: Proceedings of the 2001 industrial engineering research conference, vol 2036, pp 1–8Google Scholar
  13. 13.
    Hodgson T, Trendler P (1981) Turning hardened tool steels with cubic boron nitride inserts. CIRP Ann Manuf Technol 30(1):63–66CrossRefGoogle Scholar
  14. 14.
    Huang Y, Liang SY (2005) Modeling of cutting forces under hard turning conditions considering tool wear effect. Trans Asme 1(27):262–270Google Scholar
  15. 15.
    Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. Glob Optim 13(4):455–492zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Kishawy HA, Haglund A, Balazinski M (2006) Modelling of material side flow in hard turning. CIRP Ann Manuf Technol 55(1):85–88CrossRefGoogle Scholar
  17. 17.
    Koehler JR, Owen AB (1996) Computer experiments. In: SG, RCR (eds) Handbook of statistics, vol 13. Elsevier Science B.V. AmsterdamGoogle Scholar
  18. 18.
    Koenig W, Wand T (1986) Hartdrehen von waelzlagerstahl mit pkb und schneidkeramik. Industrie-Diamanten-Rundschau 20:67–74Google Scholar
  19. 19.
    Koenig W, Komanduri R, Tönshoff H, Ackershott G (1984) Machining of hard materials. CIRP Ann Manuf Technol 33(2):417–427Google Scholar
  20. 20.
    Lin W, Lee B, Wu C (2001) Modeling the surface roughness and cutting force for turning. J Mater Process Technol 108(3):286–293CrossRefGoogle Scholar
  21. 21.
    Liu K, Melkote S (2006) Effect of plastic side flow on surface roughness in micro-turning process. Int J Mach Tools Manuf 46:1778–1785CrossRefGoogle Scholar
  22. 22.
    McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. In: Technometrics 42, ISSN:0040-1706, pp 55–61Google Scholar
  23. 23.
    Molinari A, Nouari M (2002) Modeling of tool wear by diffusion in metal cutting. Wear 252:135–149CrossRefGoogle Scholar
  24. 24.
    Muthukrishnan N, Davim JP (2009) Optimization of machining parameters of al/sic-mmc with anova and ann analysis. J Mater Process Technol 209:225–232CrossRefGoogle Scholar
  25. 25.
    Nakai T, Nakatani S, Tomita K, Goto M (1991) Hard turning by pcbn. Conference Superabrasives ’91, SME-technical paper MR91-190, Chicago, Illinois, pp 1–15Google Scholar
  26. 26.
    Nakayama K, Arai M, Kanda T (1988) Machining characteristics of hard materials. CIRP Ann Manuf Technol 37(1):89–92CrossRefGoogle Scholar
  27. 27.
    Nalbant M, Gokkaya H, Toktas I (2007) Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in cnc turning. Model Simul Eng 1:1–14CrossRefGoogle Scholar
  28. 28.
    Narutaki N, Yamane Y (1979) Tool wear cutting temperature of cbn tools in machining of hardened steels. CIRP Ann Manuf Technol 28(1):23–28Google Scholar
  29. 29.
    Nowag L, Soelter J, Brinksmeier E (2007) Influence of turning parameters on distortion of bearing rings. Prod Eng Res Dev 1:135–139CrossRefGoogle Scholar
  30. 30.
    Oezel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479CrossRefGoogle Scholar
  31. 31.
    Oezel T, Hsu TK, Zeren E (2005) Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened aisi h13 steel. International J Adv Manuf Technol 25:262–269CrossRefGoogle Scholar
  32. 32.
    Park YW (2002) Tool material dependence of hard turning on the surface quality. Int J Korean Soc Precision Eng 3:76–82Google Scholar
  33. 33.
    Pavel R, Marinescu I, Deis M, Pillar J (2005) Effect of tool wear on surface finish for a case of continuous and interrupted hard turning. J Mater Process Technol 170:341–349CrossRefGoogle Scholar
  34. 34.
    Penalva M, Arizmendi M, Diaz F, Fernandez J (2002) Effect of tool wear on roughness in hard turning. CIRP Ann Manuf Technol 51(1):57–60CrossRefGoogle Scholar
  35. 35.
    Rahman M, Wong YS, Zareena AR (2003) Machinability of titanium alloys. Jpn Soc Mech Eng Int J 46(1):107–115Google Scholar
  36. 36.
    Ravindra H, Srinivasa Y, Krishnamurthy R (1993) Modelling of tool wear based on cutting forces in turning. Wear 169:25–32CrossRefGoogle Scholar
  37. 37.
    Risbood K, Dixit U, Sahasrabudhe A (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132(1–3):203–214CrossRefGoogle Scholar
  38. 38.
    Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4:409–435zbMATHCrossRefMathSciNetGoogle Scholar
  39. 39.
    Santner TJ, Williams BJ, Notz WI (2003) Design and analysis of computer experiments. Springer Series in Statistics. Springer, New YorkzbMATHGoogle Scholar
  40. 40.
    Soelter J, Brinksmeier E (2005) Parameter study of high speed turning of hardened steel. Prod Eng Res Dev 12(1):51–54Google Scholar
  41. 41.
    Stovicek D (1992) Hard-part turning. eliminates grinding, improves quality. Tool Prod Mag Metalworking Manuf 57:25–26Google Scholar
  42. 42.
    Suresh P, Rao PV, Deshmukh S (2002) A genetic algoritmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 42:675–680CrossRefGoogle Scholar
  43. 43.
    Tamizharasan T, Selvaraj T, Noorul Haq A, (2006) Analysis of tool wear and surface finish in hard turning. Int J Adv Manuf Technol 28:671–679CrossRefGoogle Scholar
  44. 44.
    Tosun N, Oezler L (2002) A study of tool life in hot machining using artificial neural networks and regression analysis method. J Mater Process Technol 124:99–104CrossRefGoogle Scholar
  45. 45.
    Wagner T, Paßmann D, Weinert K, Biermann D, Bledzki A (2008) Efficient modeling and optimization of the property gradation of self-reinoforced polypropylene sheets within a thermo-mechanical compaction process. In: Proceedings of the 6th CIRP international conference on intelligent computation in manufacturing engineering, pp 447–452Google Scholar
  46. 46.
    Wang X, Feng C (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356CrossRefMathSciNetGoogle Scholar
  47. 47.
    Warnecke G, Bach P (1988) Mechanical and material influences on machined surface in precision turning of steel with ceramics. In: Transactions of the North American research conference, vol 16, pp 209–216Google Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2010

Authors and Affiliations

  1. 1.Institute of Machining TechnologyTechnische Universität DortmundDortmundGermany

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