Abstract
Design of process parameters to meet the required specification of quality characteristics has become a crucial issue for many industries. The underlying relationships between process inputs and outputs are one of the main tasks in quality engineering. Most of researches assume independency and consider same relative importance of quality characteristics with constant variances over experimental space. This study represents a novel robust approach based on desirability function and global criterion methods that not only obtains the parameter design but also considers different variance, correlation, and relative importance level of outputs. The suggested method enforces all quality measurements to fall within specification limits. To illustrate computational aspects of the proposed method, two realistic examples have been conducted. The obtained results demonstrate the superiority of the proposed approach with respect to the existing approaches.
Similar content being viewed by others
References
Garcia-Diaz A, Hogg GL, Tari FG (1981) Combining simulation and optimization to solve the multimachine interference problem. Simulation 28:193–201
Greenwood AG, Rees LP, Crouch IWM (1993) Separating the art and science in simulation optimization: a knowledge- based architecture providing for machine learning. IIE Trans 25:70–83
Smith DE (1973) An empirical investigation of optimum-seeking in the computer simulation situation. Oper Res 19:362–371
Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12:214–219
Plante RD (1999) Multicriteria models for the allocation of design parameter targets. Eur J Oper Res 115:98–112
Pan JN, Pan J, Lee CY (2009) Finding and optimising the key factors for the multiple-response manufacturing process. Int J Prod Res 47:2327–2344
Ch’ng CK, Quah SH, Low HC (2005) A new approach for multiple-response optimization. Qual Eng 17:621–626
Fogliatto FS (2008) Multiresponse optimization of products with functional quality characteristics. Qual Reliab Eng Int 24:927–939
Harrington E Jr (1965) The desirability function. Ind Qual Control 21:494–498
Derringer GC (1994) A balancing act: optimising a product’s properties. Qual Prog 24:51–58
Kim KJ, Lin DKJ (2006) Optimization of multiple responses considering both location and dispersion effects. Eur J Oper Res 169:133–145
Kim KJ, Lin DKJ (2000) Simultaneous optimization of mechanical properties of steel by polynomial regression functions. J Appl Stat 49:311–325
Jeong I, Kim K (2009) An interactive desirability function method to multiresponse optimization. Eur J Oper Res 195:412–426
He Z, Wang J, Oh J, Park SH (2010) Robust optimization for multiple responses using response surface methodology. Appl Stoch Model Bus Ind 26:157–171
Sharma N, Khanna R, Gupta RD, Sharma R (2013) Modeling and multiresponse optimization on WEDM for HSLA by RSM. Int J Adv Manuf Technol 67:2269–2281
Dinesh Babu P, Buvanashekaran G, Balasubramanian KR (2013) Experimental investigation of laser transformation hardening of low alloy steel using response surface methodology. Int J Adv Manuf Technol 67:1883–1897
Pignatiello J (1993) Strategies for robust multiresponse quality engineering. IIE Trans 25:5–15
Ames A, Mattucci N, McDonald S, Szonyi G, Hawkins D (1997) Quality loss function for optimization across multiple response surfaces. J Qual Technol 29:339–346
Tsui KL (1999) Robust design optimization for multiple characteristic problems. Int J Prod Res 37:433–445
Wu FC, Chyu CC (2004) Optimization of robust design for multiple quality characteristics. Int J Prod Res 42:337–354
Chang YC, Liu CT, Hung WL (2009) Optimization of process parameters using weighted convex loss functions. Eur J Oper Res 196:752–763
Aggarwal ML, Bansal A (1998) Robust response surface design for quantitative and qualitative factors. Commun Stat Theory Methods 27:89–106
Vining G (1998) A compromise approach to multiresponse optimization. J Qual Technol 30:309–313
Ko YH, Kim KJ, Jun CH (2005) A new loss function-based method for multiresponse optimization. J Qual Technol 37:50–59
Bera S, Mukherjee I (2012) An ellipsoidal distance-based search strategy of ants for nonlinear single and multiple response optimization problems. Eur J Oper Res 223:321–332
Fung CP, Kang PC (2005) Multi-response optimization in friction properties of PBT composites using Taguchi method and principal component analysis. J Mater Process Technol 170:602–610
Routara BC, Mohanty SD, Datta S, Bandyopadhyay A, Mahapatra SS (2010) Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics of UNS C34000 brass in cylindrical grinding. Int J Adv Manuf Technol 51:135–143
Ribeiro JS, Teofilo RF, Augusto F, Ferreira MMC (2010) Simultaneous optimization of the microextraction of coffee volatiles using response surface methodology and principal component analysis. Chemometr Intell Lab Syst 102:45–52
Tatjana VS, Vidosav DM, Zoran DM (2011) An intelligent approach to robust multi-response process design. Int J Prod Res 17:5079–5097
Su CT, Tong LI (1997) Multi-response robust design by principal component analysis. Total Qual Manag 8:409–416
Antony J (2000) Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Qual Reliab Eng Int 16:3–8
Liao HC (2006) Multi-response optimization using weighted principal component. Int J Adv Manuf Technol 27:720–725
Datta S, Nandi G, Bandyopadhyay A, Pal PK (2009) Application of PCA-based hybrid Taguchi method for correlated multicriteria optimization of submerged arc weld: a case study. Int J Adv Manuf Technol 45:276–286
Gauri SK, Pal S (2014) The principal component analysis (PCA)-based approaches for multi-response optimization: some areas of concerns. Int J Adv Manuf Technol 70:1875–1887
Kazemzadeh RB, Bashiri M, Atkinson AC, Noorosana R (2008) A general frame work for multi response optimization problems based on goal programming. Eur J Oper Res 189:421–429
Amiri M, Karimi N, Jamshidi SF (2008) A methodology for optimizing statistical multi-response problems using genetic local search algorithm through fuzzy goal programming. J Appl Sci 8:3199–3206
Hejazi TH, Bashiri M, Noghondarian K, Atkinson AC (2011) Multiresponse optimization with consideration of probabilistic covariates. J Qual Reliab Eng Int 27:437–449
Hejazi TH, Bashiri M, Diaz-Garcia JA, Noghondarian K (2012) Optimization of probabilistic multiple response surfaces. Appl Math Model 36:1275–1285
Xu K, Lin DKJ, Tang LC, Xie M (2004) Multi-response system optimization using a goal attainment approach. IIE Trans 36:433–445
Tong LI, Chen CC, Wang CH (2007) Optimization of multi-response processes using the VIKOR method. Int J Adv Manuf Technol 31:1049–1057
Salmasnia A, Moeini A, Mokhtari H, Mohebbi C (2013) A robust posterior preference decision-making approach to multiple response process design. Int J Appl Decis Sci 6:186–207
Tong LI, Su CT (1997) Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Qual Reliab Eng Int 14:25–34
Liao HC (2004) A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method. Com Ind Eng 46:817–835
Gutierrez E, Lozano S (2010) Data envelopment analysis of multiple response experiments. Appl Math Model 34:1139–1148
Sahu J, Mohanty CP, Mahapatra SS (2013) A DEA approach for optimization of multiple responses in electrical discharge machining of AISI D2 steel. Procedia Eng 51:585–591
Salmasnia A, Bashiri M, Salehi M (2013) A robust interactive approach to optimize multiple correlated responses. Int J Adv Manuf Technol 67:1923–1935
Yadav OP, Bhamare SS, Rathore A (2010) Reliability-based robust design optimization: a multi-objective framework using hybrid quality loss function. Qual Reliab Eng Int 26:27–41
AL-Refaie A (2012) Optimization performance with multiple responses using cross-evaluation and aggressive formulation in data envelopment analysis. IIE Trans 44:262–276
Tong LI, Wang CH, Chen HC (2005) Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution. Int J Adv Manuf Technol 27:407–414
Wang CH (2007) Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model. Int J Adv Manuf Technol 32:617–624
Salmasnia A, Baradaran Kazemzadeh R, Akhavan Niaki ST (2012) An approach to optimize correlated multiple responses using principal component analysis and desirability function. Int J Adv Manuf Technol 62:835–846
Salmasnia A, Baradaran Kazemzadeh R, Seyyed-Esfahani M, Hejazi TH (2013) Multiple response surface optimization with correlated data. Int J Adv Manuf Technol 64:841–855
Salmasnia A, Baradaran Kazemzadeh R, Mohajer Tabrizi M (2012) A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics. Neurocomputing 91:56–66
Saaty TL (1996) Decision making with dependence and feedback: the analytic network process. RWS Publications, Pittsburgh
Harper D, Kosbe M, Peyton L (1987) Optimization of ford taurus wheel cover balance, fifth symposium on Taguchi methods 527–539
Díaz-García JA, Bashiri M (2013) Multiple response optimisation: an approach from multiobjective stochastic programming. Appl Math Model. doi:10.1016/j.apm.2013.10.010
Chiao C, Hamada M (2001) Analyzing experiments with correlated multiple responses. J Qual Technol 33:451–465
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salmasnia, A., Bashiri, M. A new desirability function-based method for correlated multiple response optimization. Int J Adv Manuf Technol 76, 1047–1062 (2015). https://doi.org/10.1007/s00170-014-6265-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-014-6265-x