A proposed satisfaction function model to optimize process performance with multiple quality responses in the Taguchi method

The Taguchi method has been found effective for optimizing a single quality response. In reality, customers are concerned about multiple quality responses of a product. Although several approaches have been proposed to deal with this issue, however they ignored engineers’ satisfaction regarding process factor settings. This research, therefore, proposes an approach for optimizing multiple responses in the application of the Taguchi method. The mathematical relationships between each response quality and process factors are first formulated. Then, a proper satisfaction function is selected to represent each response and process factor. A complete optimization model is developed. Three case studies are provided for illustration; in all of which the proposed approach provides the largest improvement percentages while considering process engineers’ satisfaction about process factors. Compared to previous approaches in literature, such as gray analysis and fuzzy-gray analysis, the proposed approach provides optimal solution within factor setting ranges, relies on mathematical relationships between response and process factors, and considers engineers’ preference regarding process factor settings. Definitely, the proposed approach shall provide practitioners a great assistance in optimizing performance with multiple responses while considering their preferences about responses as well as process factor settings.

[1]  Ko-Ta Chiang,et al.  The optimal process conditions of an injection-molded thermoplastic part with a thin shell feature using grey-fuzzy logic: A case study on machining the PC/ABS cell phone shell , 2007 .

[2]  Jean-Marc Martel,et al.  Incorporating the Decision-maker's Preferences in the Goal-programming Model , 1990 .

[3]  Muhittin Oral,et al.  A Linearization Procedure for Quadratic and Cubic Mixed-Integer Problems , 1992, Oper. Res..

[4]  Bheemappa Suresha,et al.  Grey-based Taguchi approach for wear assessment of SiC filled carbon–epoxy composites , 2012 .

[5]  Abbas Al-Refaie,et al.  Optimising correlated QCHs in robust design using principal components analysis and DEA techniques , 2011 .

[6]  Mahdi Bashiri,et al.  A goal programming-TOPSIS approach to multiple response optimization using the concepts of non-dominated solutions and prediction intervals , 2011, Expert Syst. Appl..

[7]  Piero Zappi.  Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming , 2015 .

[8]  Abbas Al-Refaie.  Grey-data envelopment analysis approach for solving the multi-response problem in the Taguchi method , 2010 .

[9]  Kuo-Cheng Tai,et al.  Optimizing SUS 304 wire drawing process by grey relational analysis utilizing Taguchi method , 2008 .

[10]  M.-H.C. Li,et al.  DMAIC Approach to Improve the Capability of SMT Solder Printing Process , 2008, IEEE Transactions on Electronics Packaging Manufacturing.

[11]  Habib Chabchoub,et al.  Serial correlation estimation through the imprecise Goal Programming model , 2007, Eur. J. Oper. Res..

[12]  Abbas Al-Refaie,et al.  Data envelopment analysis approaches for solving the multiresponse problem in the Taguchi method , 2009, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[13]  Habib Chabchoub,et al.  Quality control system design through the goal programming model and the satisfaction functions , 2008, Eur. J. Oper. Res..

[14]  Fouad Ben Abdelaziz,et al.  Decision-maker's preferences modeling in the stochastic goal programming , 2005, Eur. J. Oper. Res..

[15]  Xiang Chen,et al.  Numerical optimization of gating system parameters for a magnesium alloy casting with multiple performance characteristics , 2008 .

[16]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[17]  Abbas Al-Refaie,et al.  A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method , 2012, Int. J. Fuzzy Syst. Appl..

[18]  Chung-Feng Jeffrey Kuo,et al.  Multi-objective optimization of laser-scribed micro grooves on AZO conductive thin film using Data Envelopment Analysis , 2012 .

[19]  A. Al-Refaie,et al.  Optimizing SMT Performance Using Comparisons of Efficiency Between Different Systems Technique in DEA , 2009, IEEE Transactions on Electronics Packaging Manufacturing.

[20]  Chung-Feng Jeffrey Kuo,et al.  Optimization of the film coating process for polymer blends by the grey-based Taguchi method , 2005 .

[21]  Taïcir Loukil,et al.  Production , Manufacturing and Logistics Solving multi-criteria scheduling flow shop problem through compromise programming and satisfaction functions , 2008 .

[22]  Reza Baradaran Kazemzadeh,et al.  A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics , 2012, Neurocomputing.

[23]  Abbas Al-Refaie,et al.  Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in data envelopment analysis , 2012 .