Skip to main content
SpringerLink
Log in

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input–output patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.

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

Similar content being viewed by others

References

  1. Konig W, Berktold A, Koch KF (1993) Turning versus grinding—a comparison of surface integrity aspects and attainable accuracies. Annals of CIRP 42(1):39–43

    Article  Google Scholar 

  2. Tonshoff HK, Arendt C, Ben Amor R (2000) Cutting of hardened steel. Annals of CIRP 49(2):547–566

    Article  Google Scholar 

  3. Zou JM, Anderson M, Stahl JE (2004) Identification of cutting errors in precision hard turning process. J Mater Process Technol 153–154:746–750

    Article  Google Scholar 

  4. Rech J, Moisan A (2003) Surface Integrity in finish hard turning of case hardened steels. Int J Mach Tools Manuf 43:543–550

    Article  Google Scholar 

  5. Destefani J (2004) Technology key to mold making success. Manuf Eng 133(4):59–64

    Google Scholar 

  6. Elbestawi MA, Chen L, Becze CE, EI-Wardany TI (1997) High-speed milling of dies and molds in their hardened state. Annals of CIRP 46(1):57–62

    Article  Google Scholar 

  7. Byrne G, Dornfeld D, Denkena B (2003) Advancing cutting technology. Annals of CIRP 52(2):483–507

    Article  Google Scholar 

  8. Klocke F, Brinskmeier E, Weinert K (2005) Capability profile of hard cutting and grinding processes. Annals of CIRP 54(2):557–580

    Article  Google Scholar 

  9. GuoYB SJA (2004) Comparative study of hard turned and cylindrically ground white layers. Int J Mach Tools Manuf 44:135–145

    Article  Google Scholar 

  10. Thiele JD, Melkote SN, Peascoe RA, Watkins TR (2000) Effect of cutting edge geometry and workpiece hardness on surface residual stresses in finish hard turning of AISI 52100 steel. ASME J Manuf Sci Eng 122:642–649

    Article  Google Scholar 

  11. Huang Y, Liang SY (2003) Cutting forces modeling considering the effect of tool thermal property—application to CBN hard turning. Int J Mach Tools Manuf 43:307–315

    Article  Google Scholar 

  12. ChouYK ECJ (1997) Tool wear mechanism in continuous cutting of hardened tool steels. Wear 212:59–65

    Article  Google Scholar 

  13. 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 

  14. Thiele JD, Melkote SN (1999) Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel. J Mater Process Technol 94:216–226

    Article  Google Scholar 

  15. Ozel T, Hsu T-K, Zeren E (2000) 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. Int J Adv Manuf Technol 25(3–4):262–269

    Google Scholar 

  16. Kishawy HA, Elbestawi MA (2000) Tool wear and surface integrity during high-speed turning hardened steel with polycrystalline cubic boron nitride tools. J Eng Manuf 215:755–767

    Article  Google Scholar 

  17. EI-Wardany TI, Kishawy HA, Elbestawi MA (2000) Surface integrity of die material in high-speed hard machining, part 1, micro graphical analysis. ASME J Manuf Sci Eng 122(4):620–631

    Article  Google Scholar 

  18. EI-Wardany TI, Kishawy HA, Elbestawi MA (2000) Surface integrity of die material in high-speed hard machining, part 2, micro hardness variations and residual stresses. ASME J Manuf Sci Eng 122(4):632–641

    Article  Google Scholar 

  19. Poulachon G, Bandyopadhyay BP, Jawahir IS, Pheulpin S, Seguin E (2003) The influence of the microstructure of hardened tool steel workpiece on the wear of PCBN cutting tools. Int J Mach Tools Manuf 43:139–144

    Article  Google Scholar 

  20. 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 steel. J Mater Process Technol 169:388–395

    Article  Google Scholar 

  21. 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 170:341–349

    Article  Google Scholar 

  22. Diniz AE, Oliveira AJ (2008) Hard turning of interrupted surfaces using CBN tools. J Mater Process Technol 95:275–281

    Article  Google Scholar 

  23. Chou YK, Song H (2004) Tool nose radius effects on finish turning. J Mater Process Technol 148:259–268

    Article  Google Scholar 

  24. Kumar AS, Durai R, Sornakumar T (2003) Machinability of hardened steel using alumina based ceramic cutting tools. Int J Refract Met Hard Mater 21:109–117

    Article  Google Scholar 

  25. Benga GC, Abrao AM (2003) Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools. J Mater Process Technol 143–144:237–241

    Article  Google Scholar 

  26. Grzesik W, Wanat T (2005) Comparative assessment of surface roughness produced by hard machining with mixed ceramic tools including 2D and 3D analysis. J Mater Process Technol 169:364–371

    Article  Google Scholar 

  27. Grzesik W, Wanat T (2006) Hard turning of quenched alloy steel parts using conventional and wiper ceramic inserts. Int J Mach Tools Manuf 46:1988–1995

    Article  Google Scholar 

  28. Grzesik W (2008) Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools. Wear 265:327–335

    Article  Google Scholar 

  29. Schwach DW, Guo YB (2005) Feasibility of producing optimal surface integrity by process design in hard turning. Mater Sci Eng 395:116–123

    Article  Google Scholar 

  30. Quiza R, Figueira L, Davim JP (2008) Comparing statistical models and artificial networks on predicting the tool wear in hard machining D2 AISI steel. Int J Adv Manuf Technol 37:641–648

    Article  Google Scholar 

  31. Ozel 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 4:467–479

    Article  Google Scholar 

  32. Davim JP, Figueira L (2007) Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques. Mater Des 28:1186–1191

    Article  Google Scholar 

  33. Klocke F, Kratz H (2005) Advanced tool edge geometry for high precision hard turning. Annals of CIRP 54(1):47–50

    Article  Google Scholar 

  34. Ozel T, Karpat Y, Figueira L, Davim JP (2007) Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Process Technol 189:192–198

    Article  Google Scholar 

  35. Davim JP, Figueira L (2007) Comparative evaluation of conventional and wiper ceramic tools on cutting forces, surface roughness and tool wear in hard turning AISI D2 steel. J Eng Manuf 221:625–633

    Article  Google Scholar 

  36. Gaitonde VN, Karnik SR, Figueira L, Davim JP (2009) Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts. Int J Refract Met Hard Mater 27(4):754–763

    Article  Google Scholar 

  37. Montgomery DC (2004) Design and analysis of experiments, 5th edn. Wiley, New York

    Google Scholar 

  38. Karnik SR, Gaitonde VN, Davim JP (2008) A Comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling. Int J Adv Manuf Technol 38:868–883

    Article  Google Scholar 

  39. Karnik SR, Gaitonde VN, Mata F, Davim JP (2008) Investigative study on machinability aspects of unreinforced and reinforced PEEK composite machining using ANN model. J Reinf Plast Compos 27(7):751–768

    Article  Google Scholar 

  40. Karnik SR, Gaitonde VN, Rubio JC, Correia AE, Abrao AM, Davim JP (2008) Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model. Mater Des 29:1768–1776

    Article  Google Scholar 

  41. Davim JP, Gaitonde VN, 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 205:16–23

    Article  Google Scholar 

  42. Schalkoff RB (1997) Artificial neural networks. McGraw-Hill, Singapore

    MATH  Google Scholar 

  43. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms and applications. Prentice–Hall, New York

    MATH  Google Scholar 

  44. Math Works Incorporation (2005) MATLAB User Manual, Version 7.1, R 14. Natick, MA

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Production Engineering, B. V. B. College of Engineering and Technology, Hubli, 580 031, Karnataka, India

    Vinayak Neelakanth Gaitonde

  2. Department of Electrical and Electronics Engineering, B. V. B. College of Engineering and Technology, Hubli, 580 031, Karnataka, India

    S. R. Karnik

  3. Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193, Aveiro, Portugal

    Luis Figueira & J. Paulo Davim

Authors
  1. Vinayak Neelakanth Gaitonde
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. R. Karnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis Figueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Paulo Davim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinayak Neelakanth Gaitonde.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gaitonde, V.N., Karnik, S.R., Figueira, L. et al. Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. Int J Adv Manuf Technol 52, 101–114 (2011). https://doi.org/10.1007/s00170-010-2714-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2714-3

Keywords

Search

Navigation