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Multivariate process capability analysis applied to AISI 52100 hardened steel turning

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Abstract

Hard turning operations have been extensively investigated owing to their ability to reduce process cycle time, increase process flexibility, ensure high-dimensional accuracy, and enable machining without a cutting fluid. These processes are rather common for dealing with multiple quality characteristics. To evaluate the process ability and meet customer needs, multivariate statistical techniques are recommended for estimating the capability indices. Principal component analysis can be applied to reducing the problem dimension and estimate process capability indices. The aim of this study was to assess the capability of AISI 52100 hardened steel turning operations and achieve process specifications. Multivariate process capability indices were calculated to assess five roughness parameters of surface finishing. By using a weighted approach of principal component analysis, a new method is proposed for estimating the process capability indices. The results highlight not only the relevance of conducting a multivariate capability analysis in the case of actual machining but also how successfully the proposed method was performed.

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References

  1. Paiva AP, Paiva EJ, Ferreira JR, Balestrassi PP, Costa SC (2009) A multivariate mean square error optimization of AISI 52100 hardened steel turning. Int J Adv Manuf Technol 43(7-8):631–643. https://doi.org/10.1007/s00170-008-1745-5

    Article  Google Scholar 

  2. Huang Y, Chou YK, Liang SY (2007) CBN tool wear in hard turning: a survey on research progresses. Int J Adv Manuf Technol 35(5-6):443–453. https://doi.org/10.1007/s00170-006-0737-6

    Article  Google Scholar 

  3. Tamizharasan T, Selvaraj T, Haq AN (2006) Analysis of tool wear and surface finish in hard turning. Int J Adv Manuf Technol 28(7-8):671–679. https://doi.org/10.1007/s00170-004-2411-1

    Article  Google Scholar 

  4. Singh D, Rao PV (2007) A surface roughness prediction model for hard turning process. Int J Adv Manuf Technol 32(11-12):1115–1124. https://doi.org/10.1007/s00170-006-0429-2

    Article  Google Scholar 

  5. Bouacha K, Yallese MA, Mabrouki T, Rigal JF (2010) Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int J Refract Met Hard Mater 28(3):349–361. https://doi.org/10.1016/j.ijrmhm.2009.11.011

    Article  Google Scholar 

  6. Saini S, Ahuja IS, Sharma VS (2012) Residual stresses, surface roughness, and tool wear in hard turning: a comprehensive review. Mater Manuf Process 27(6):583–598. https://doi.org/10.1080/10426914.2011.585505

    Article  Google Scholar 

  7. Paiva AP, Campos PH, Ferreira JR, Lopes LGD, Paiva EJ, Balestrassi PP (2012) A multivariate robust parameter design approach for optimization of AISI 52100 hardened steel turning with wiper mixed ceramic tool. Int J Refract Met Hard Mater 30(1):152–163. https://doi.org/10.1016/j.ijrmhm.2011.08.001

    Article  Google Scholar 

  8. 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. https://doi.org/10.1016/j.ijrmhm.2008.12.007

    Article  Google Scholar 

  9. Motorcu AR, Güllü A (2006) Statistical process control in machining, a case study for machine tool capability and process capability. Mater Des 27(5):364–372. https://doi.org/10.1016/j.matdes.2004.11.003

    Article  Google Scholar 

  10. KT Y, Sheu SH, Chen KS (2007) The evaluation of process capability for a machining center. Int J Adv Manuf Technol 33(5-6):505–510. https://doi.org/10.1007/s00170-006-0481-y

    Article  Google Scholar 

  11. Chen J, Zhu F, Li GY, Ma YZ, Tu YL (2012) Capability index of a complex-product machining process. Int J Prod Res 50(12):3382–3394. https://doi.org/10.1080/00207543.2011.578165

    Article  Google Scholar 

  12. Kahraman F, Esme U, Kulekci MK, Kazancoglu Y (2012) Process capability analysis in machining for quality improvement in turning operations. Mater Test 54(2):120–125. https://doi.org/10.3139/120.110306

    Article  Google Scholar 

  13. Pan JN, Li CI (2014) New capability indices for measuring the performance of a multidimensional machining process. Expert Syst Appl 41(5):2409–2414. https://doi.org/10.1016/j.eswa.2013.09.039

    Article  Google Scholar 

  14. CW W, Pearn WL, Kotz S (2009) An overview of theory and practice on process capability indices for quality assurance. Int J Prod Econ 117(2):338–359. https://doi.org/10.1016/j.ijpe.2008.11.008

    Article  Google Scholar 

  15. Montgomery DC (2009) Introduction to statistical quality control, 6th edn. John Wiley & Sons, Hoboken

    MATH  Google Scholar 

  16. Pearn WL, Kotz S (2006) Encyclopedia and handbook of process capability indices - a comprehensive exposition of quality control measures. https://doi.org/10.1142/9789812773753

  17. Peruchi RS, Paiva AP, Balestrassi PP, Ferreira JR, Sawhney R (2014) Weighted approach for multivariate analysis of variance in measurement system analysis. Precis Eng 38(3):651–658. https://doi.org/10.1016/j.precisioneng.2014.03.001

    Article  Google Scholar 

  18. Veiga P, Mendes L, Lourenço L (2015) A retrospective view of statistical quality control research and identification of emerging trends: a bibliometric analysis. Qual Quant 50(2):673–692. https://doi.org/10.1007/s11135-015-0170-8

    Article  Google Scholar 

  19. Wang FK, Chen JC (1998) Capability index using principal components analysis. Capability Index Using Principal Components Qual Eng 11(1):37–41. https://doi.org/10.1080/08982119808919208

    Google Scholar 

  20. Wang FK (2006) Quality evaluation of a manufactured product with multiple characteristics. Qual Reliab Eng Int 22(2):225–236. https://doi.org/10.1002/qre.712

    Article  Google Scholar 

  21. Dharmasena LS, Zeephongsekul P (2015) A new process capability index for multiple quality characteristics based on principal components. Int J Prod Res 7543(15):1–17. https://doi.org/10.1080/00207543.2015.1091520

    Google Scholar 

  22. Pearn WL, Wang FK, Yen CH (2007) Multivariate capability indices: distributional and inferential properties. J Appl Stat 34(8):941–962. https://doi.org/10.1080/02664760701590475

    Article  MathSciNet  Google Scholar 

  23. Guevara RD, Vargas JA (2015) Evaluation of process capability in multivariate simple linear profiles. J Stat Comput Simul 19(6):1–18. https://doi.org/10.1016/j.scient.2012.09.010

    Google Scholar 

  24. Wang FK (2010) A general procedure for process yield with multiple characteristics. IEEE Trans Semicond Manuf 23(4):503–508. https://doi.org/10.1109/TSM.2010.2057264

    Article  Google Scholar 

  25. Wang FK (2012) Estimating the process yield of multiple characteristics with one-sided specifications. IEEE Trans Semicond Manuf 25(1):57–62. https://doi.org/10.1109/TSM.2011.2169093

    Article  Google Scholar 

  26. Santos-Fernández E, Scagliarini M (2012) MPCI: an R package for computing multivariate process capability indices. J Stat Softw 47(2):1–15. https://doi.org/10.1359/JBMR.0301229

    Google Scholar 

  27. Haridy S, Wu Z, Castagliola P (2011) Univariate and multivariate approaches for evaluating the capability of dynamic-behavior processes (case study). Stat Methodol 8(2):185–203. https://doi.org/10.1016/j.stamet.2010.09.003

    Article  MathSciNet  Google Scholar 

  28. Scagliarini M (2011) Multivariate process capability using principal component analysis in the presence of measurement errors. AStA Adv Stat Anal 95(2):113–128. https://doi.org/10.1007/s10182-011-0156-3

    Article  MathSciNet  Google Scholar 

  29. Wang FK, TCT D (2000) Using principal component analysis in process performance for multivariate data. Omega 28(2):185–194. https://doi.org/10.1016/S0305-0483(99)00036-5

    Article  Google Scholar 

  30. Tano I, Vannman K (2013) A multivariate process capability index based on the first principal component only. Qual Reliab Eng Int 29(7):987–1003. https://doi.org/10.1002/qre.1451

    Article  Google Scholar 

  31. Perakis M, Xekalaki E (2012) On the implementation of the principal component analysis-based approach in measuring process capability. Qual Reliab Eng Int 28(4):467–480. https://doi.org/10.1002/qre.1260

    Article  Google Scholar 

  32. Zhang M, Wang GA, He S, He Z (2014) Modified multivariate process capability index using principal component analysis. Chinese J Mech Eng 27(2):249–259. https://doi.org/10.3901/CJME.2014.02.249

    Article  Google Scholar 

  33. Tano I, Vannman K (2012) Comparing confidence intervals for multivariate process capability indices. Qual Reliab Eng Int 28(4):481–495. https://doi.org/10.1002/qre.1250

    Article  Google Scholar 

  34. Dianda DF, Quaglino MB, Pagura JA (2016) Performance of multivariate process capability indices under normal and non-normal distributions. Qual Reliab Eng Int 33(2):275–295. https://doi.org/10.1002/qre.2003

    Article  Google Scholar 

  35. Paiva AP, Gomes JHF, Peruchi RS et al (2014) A multivariate robust parameter optimization approach based on principal component analysis with combined arrays. Comput Ind Eng 74:186–198. https://doi.org/10.1016/j.cie.2014.05.018

    Article  Google Scholar 

  36. Vannman K (1995) A unified approach to capability indices. Stat Sin 5:805–820 http://www3.stat.sinica.edu.tw/statistica/j5n2/j5n227/j5n227.htm

    MATH  Google Scholar 

  37. Wang CH (2005) Constructing multivariate process capability indices for short-run production. Int J Adv Manuf Technol 26(11-12):1306–1311. https://doi.org/10.1007/s00170-004-2397-8

    Article  Google Scholar 

  38. Peruchi RS, Balestrassi PP, De Paiva AP et al (2013) A new multivariate gage R&R method for correlated characteristics. Int J Prod Econ 144(1):301–315. https://doi.org/10.1016/j.ijpe.2013.02.018

    Article  Google Scholar 

  39. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844. https://doi.org/10.1016/S0890-6955(03)00059-2

    Article  Google Scholar 

  40. Peruchi RS, Balestrassi PP, Paiva AP et al (2013) A new multivariate gage R&R method for correlated characteristics. Int J Prod Econ 144(1):301–315. https://doi.org/10.1016/j.ijpe.2013.02.018

    Article  Google Scholar 

  41. Kaya I, Kahraman C (2010) A new perspective on fuzzy process capability indices: robustness. Expert Syst Appl 37(6):4593–4600. https://doi.org/10.1016/j.eswa.2009.12.049

    Article  Google Scholar 

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Funding

The authors would like to express their gratitude to the Brazilian agencies Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for supporting this research.

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Peruchi, R.S., Rotela Junior, P., Brito, T.G. et al. Multivariate process capability analysis applied to AISI 52100 hardened steel turning. Int J Adv Manuf Technol 95, 3513–3522 (2018). https://doi.org/10.1007/s00170-017-1458-8

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  • DOI: https://doi.org/10.1007/s00170-017-1458-8

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