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Optimal variable screening in automobile motor-head machining process using metaheuristic approaches in the Mahalanobis-Taguchi System

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Abstract

The Mahalanobis-Taguchi System is a data analytical method for the diagnosis and/or pattern recognition with multivariate data and it is useful for quantitative decision making where the Mahalanobis distance plays a key role. Over time, MTS has received wide acceptance in the scientific community as well as in practical industry and it has been applied to different problems where variable screening of the original set of attributes is essential. In this paper, MTS is applied to an automobile motor-head machining process and the corresponding mathematical model for dimensional reduction is solved using metaherustic algorithms from swarm intelligence optimization.

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References

  1. Cudney E A, Jugulum R, Paryani K (2006) Applying the Mahalanobis-Taguchi system to vehicle handling. Concurr Engin Res Appl 14(4):343–354

    Article  Google Scholar 

  2. Das P, Datta S (2010) A statistical concept in determination of threshold value for future diagnosis in MTS: An alternative to Taguchi’s loss function approach 4(2):95–103

  3. Deep K, Chauhan P, Pant M (2012) Multi task selection including part mix, tool allocation and process plans in CNC machining centers using new binary PSO. IEEE World Congress Comput Intell 10–15

  4. Foster C R, Jugulum R, Frey DD (2009) Evaluating an adaptive One-Factor-At-a-Time search procedure within the Mahalanobis-Taguchi System. Int J Indus Syst Eng 4(6):600–614

    Google Scholar 

  5. Ghasemi E, Asghaie A, Cudney E (2015) Mahalanobis Taguchi system: a review 32(3):291–307

    Google Scholar 

  6. Iquebal A S, Pal A, Ceglarek D, Tiwari MK (2014) Enhancement of Mahalanobis–Taguchi system via rough sets based feature selection. Experts Syst Appl 41:8003–8015

    Article  Google Scholar 

  7. Jin X, Chow TWS (2013) Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis–Taguchi system 40(15):5787–5795

    Google Scholar 

  8. Kennedy J, Eberhart R C (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  9. Kennedy J, Eberhart R C (1997) A discrete binary version of the particle swarm algorithm. IEEE Int Conf Syst Man Cybern 5:4104–4108

    Google Scholar 

  10. Nguyen B H, Xue B, Andreae P (2017) A novel binary particle swarm algorithm and its aplications on knapsack and feature selection problems. Intell Evolut Syst Proc Adaptn Learn Optim 8:319–332

    Google Scholar 

  11. Pal A, Maiti J (2010) Development of a hybrid methodology for dimensionality reduction in Mahalanobis-Taguchi system using Mahalanobis distance and binary particle swarm optimization. Expert Syst Appl 37(2):1286–1293

    Article  Google Scholar 

  12. Rai B, Chinnam R, Singh N (2008) Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis 3(2):134–148

    Google Scholar 

  13. Ramlie F, Jamaludin K, Dolah R (2016) Optimal feature selection of taguchi character recognition in the Mahalanobis-Taguchi system using bees algorithm 12(3):2651–2671

    Google Scholar 

  14. Reséndiz-Flores E O, López-Quintero ME (2017) Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach. Int J Adv Manuf Technol 90(1):437–443

    Article  Google Scholar 

  15. Reséndiz-Flores E O, Moncayo-Martínez LA, Solís G (2012) Binary ant colony optimization applied to variable screening in the Mahalanobis-Taguchi system. Expert Syst Appl 40(2):634–637

    Article  Google Scholar 

  16. Reséndiz-Flores E O, Rull-Flores CA (2013) Mahalanobis-Taguchi system applied to variable selection in automotive pedals components using gompertz binary particle swarm optimization. Expert Syst Appl 40(7):2361–2365

    Article  Google Scholar 

  17. Saraiva P, Faísca N, Costa R, Gonçalves A (2004) Fault identification in chemical processes through a modified Mahalanobis–Taguchi strategy. Comput Aided Chem Eng 18:799–804

    Article  Google Scholar 

  18. Su C, Hsiao Y (2007) An evaluation of the robustness of MTS for imbalanced data 19(10):1321–1332

    Google Scholar 

  19. Su C, Li T (2002) Neural and MTS algorithms for feature selection 3(2):113–131

  20. Taguchi G, Jugulum R (2000) New trends in multivariate diagnosis. Sankhyā Indian J Stat Series B 233–248

  21. Wang H, Chiu C, Su C (2004) Data classification using the Mahalanobis-Taguchi system 21(6):606–618

    Google Scholar 

  22. Woodall W H, Koudelik R, Tsui K L, Kim S B, Stoumbus Z G, Carvounis C P (2003) A review and analysis of the Mahalanobis-Taguchi system. Amer Stat Assoc Amer Soc Qual Technometr 45(1):1–15

    MathSciNet  Google Scholar 

  23. Xue B, Nguyen S, Zhang M (2014) A new binary particle swarm optimisation algorithm for feature selection. Eur Conf Appl Evol Comput 501–513

  24. Yang T, Cheng Y (2010) The use of Mahalanobis-Taguchi system to improve flip-chip bumping height inspection efficiency 50(3):407–414

    Google Scholar 

  25. Yazid A M, Rijal J K, Awaluddin M, Sari E (2015) Pattern recognition on remanufacturing automotive component as support decision making using Mahalanobis-Taguchi system. Procedia CIRP 26:258–263

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Postgraduate Studies and Research, Department of Industrial Engineering, The Technological Institute of Saltillo, Blvd. V. Carranza 2400 Col. Tecnológico C.P., 25280, Saltillo, Coahuila, México

    Yadira I. Reyes-Carlos, Cecilia G. Mota-Gutiérrez & Edgar O. Reséndiz-Flores

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  1. Yadira I. Reyes-Carlos
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  2. Cecilia G. Mota-Gutiérrez
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  3. Edgar O. Reséndiz-Flores
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Correspondence to Yadira I. Reyes-Carlos.

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Reyes-Carlos, Y.I., Mota-Gutiérrez, C.G. & Reséndiz-Flores, E.O. Optimal variable screening in automobile motor-head machining process using metaheuristic approaches in the Mahalanobis-Taguchi System. Int J Adv Manuf Technol 95, 3589–3597 (2018). https://doi.org/10.1007/s00170-017-1348-0

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  • DOI: https://doi.org/10.1007/s00170-017-1348-0

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