Quality Improvement Using Taguchi’s Model: – A Casy Study from Serbia
DOI:
https://doi.org/10.7250/eb.2013.011Keywords:
knowledge–based system, robust system, quality loss, genetic algorithm, improvementAbstract
The developed Taguchi’s model for the multiobjective process design could incorporate customers’ specifications for several characteristics and could be used to optimise various types of manufacturing processes. The goals of the model implementation areis is to find the optimal process parameter settings and to reduce the influence of noise factors, to ensure the achievement of the specified product characteristic values and to reduce variations. The model is given in a form of an hybrid intelligent system for the process design (optimisation, modelling and/or simulation), providing the possibility for learning features (learning from the experimental or from the historical data).
References
J. Antony, Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Qual Reliab Eng Int, 16, 3--8 (2000). http://dx.doi.org/10.1002/(SICI)1099-1638(200001/02)16:1<3::AID-QRE276>3.0.CO;2-W
C.P. Fung and P.C. Kang, Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis. J Mater Proc Technol, 170, 602--610 (2005). http://dx.doi.org/10.1016/j.jmatprotec.2005.06.040
C.H. Wang, L.I. Tong, Optimization of Dynamic Multi-Response Problems Using Grey Multiple Attribute Decision Making. Quality Engineering, 17, 1--9 (2005). http://dx.doi.org/10.1081/QEN-200028450
F.C. Wu, Optimising robust design for correlated quality characteristics. Int J Adv Manuf Technol, 24, 1--8 (2004)
T. Sibalija, V. Majstorovic, Multi-response optimisation of thermosonic copper wire-bonding process with correlated responses. Int J Adv Manuf Technol, 42, 363--371 (2009). http://dx.doi.org/10.1007/s00170-008-1595-1
L. M. Galantucci, L. Tricarico, R. Spina, A quality evaluation method for laser welding of Al alloys through neural networks. CIRP Annals, 49, 131--134 (2000). http://dx.doi.org/10.1016/S0007-8506(07)62912-6
C.M. Hsu, C.T. Su, D. Liao, Simultaneous optimisation of the broadband tap coupler optical performance based on neural networks and exponential desirability functions. Int J Adv Manuf Technol, 23, 896--902 (2004). http://dx.doi.org/10.1007/s00170-003-1733-8
L.M. Galantucci, G. Percoco, R. Spina, An artificial intelligence approach to the registration of free-form shapes. CIRP Annals, 53,139–142 (2004). http://dx.doi.org/10.1016/S0007-8506(07)60663-5
R. Roy, J. Mehnen, Dynamic multi-objective optimisation for machining gradient materials. CIRP Annals, 57, 429--432 (2008). http://dx.doi.org/10.1016/j.cirp.2008.03.020
D. Drain, W.M. Carlyle, D.C.Montgomery, C. Borror, C. Anderson-Cook, A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs. Qual Reliab Eng Int, 20, 637--650 (2004). http://dx.doi.org/10.1002/qre.573
K.W. Tong, C.K. Kwong, K.M. Yu, Intelligent process design system for the transfer moulding of electronic packages. Int J Prod Res, 42, 1911—1931 (2004). http://dx.doi.org/10.1080/00207540310001652860
T.H. Hou, S.H. Chen, T.Y. Lin, K.M. Huang, An integrated system for setting the optimal parameters in IC chip-package wire bonding processes. Int J Adv Manuf Technol, 30, 247—253 (2006). http://dx.doi.org/10.1007/s00170-005-0083-0
T. Sibalija, V. Majstorovic, Z. Miljkovic, An intelligent approach to robust multiresponse process design. Int J Prod Res (2011), vol. 49 No. 17, pp. 5079-5097. http://dx.doi.org/10.1080/00207543.2010.511476
T. Sibalija, V. Majstorovic, An integrated simulated annealing-based method for robust multiresponse process optimization, Int J Adv Manuf Technol, 2012 59 (9-12):1227-1244. http://dx.doi.org/10.1007/s00170-011-3572-3
T. Sibalija, V. Majstorovic, M. Sokovic, Taguchi-based and intelligent optimisation of a multi-response process using historical data, Strojniski Vestnik – Journal of Mechanical Engineering, (2011), vol. 57 br. 4, str. 357-365
C.P. Fung and P.C. Kang, Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis. J Mater Proc Technol, 170, 602--610 (2005). http://dx.doi.org/10.1016/j.jmatprotec.2005.06.040
C.H. Wang, L.I. Tong, Optimization of Dynamic Multi-Response Problems Using Grey Multiple Attribute Decision Making. Quality Engineering, 17, 1--9 (2005). http://dx.doi.org/10.1081/QEN-200028450
F.C. Wu, Optimising robust design for correlated quality characteristics. Int J Adv Manuf Technol, 24, 1--8 (2004)
T. Sibalija, V. Majstorovic, Multi-response optimisation of thermosonic copper wire-bonding process with correlated responses. Int J Adv Manuf Technol, 42, 363--371 (2009). http://dx.doi.org/10.1007/s00170-008-1595-1
L. M. Galantucci, L. Tricarico, R. Spina, A quality evaluation method for laser welding of Al alloys through neural networks. CIRP Annals, 49, 131--134 (2000). http://dx.doi.org/10.1016/S0007-8506(07)62912-6
C.M. Hsu, C.T. Su, D. Liao, Simultaneous optimisation of the broadband tap coupler optical performance based on neural networks and exponential desirability functions. Int J Adv Manuf Technol, 23, 896--902 (2004). http://dx.doi.org/10.1007/s00170-003-1733-8
L.M. Galantucci, G. Percoco, R. Spina, An artificial intelligence approach to the registration of free-form shapes. CIRP Annals, 53,139–142 (2004). http://dx.doi.org/10.1016/S0007-8506(07)60663-5
R. Roy, J. Mehnen, Dynamic multi-objective optimisation for machining gradient materials. CIRP Annals, 57, 429--432 (2008). http://dx.doi.org/10.1016/j.cirp.2008.03.020
D. Drain, W.M. Carlyle, D.C.Montgomery, C. Borror, C. Anderson-Cook, A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs. Qual Reliab Eng Int, 20, 637--650 (2004). http://dx.doi.org/10.1002/qre.573
K.W. Tong, C.K. Kwong, K.M. Yu, Intelligent process design system for the transfer moulding of electronic packages. Int J Prod Res, 42, 1911—1931 (2004). http://dx.doi.org/10.1080/00207540310001652860
T.H. Hou, S.H. Chen, T.Y. Lin, K.M. Huang, An integrated system for setting the optimal parameters in IC chip-package wire bonding processes. Int J Adv Manuf Technol, 30, 247—253 (2006). http://dx.doi.org/10.1007/s00170-005-0083-0
T. Sibalija, V. Majstorovic, Z. Miljkovic, An intelligent approach to robust multiresponse process design. Int J Prod Res (2011), vol. 49 No. 17, pp. 5079-5097. http://dx.doi.org/10.1080/00207543.2010.511476
T. Sibalija, V. Majstorovic, An integrated simulated annealing-based method for robust multiresponse process optimization, Int J Adv Manuf Technol, 2012 59 (9-12):1227-1244. http://dx.doi.org/10.1007/s00170-011-3572-3
T. Sibalija, V. Majstorovic, M. Sokovic, Taguchi-based and intelligent optimisation of a multi-response process using historical data, Strojniski Vestnik – Journal of Mechanical Engineering, (2011), vol. 57 br. 4, str. 357-365
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14.05.2014
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Copyright (c) 2014 Vidosav D. Majstorovic, Tatjana Sibalija (Author)
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Majstorovic, V. D., & Sibalija, T. (2014). Quality Improvement Using Taguchi’s Model: – A Casy Study from Serbia. Economics and Business, 24, 94-98. https://doi.org/10.7250/eb.2013.011
