Fuzzy neural network approach to optimizing process performance by using multiple responses

Abstract This research proposes a method for optimizing process performance; the method involves the use of multiple quality characteristics, fuzzy logic, and radial basis function neural networks (RBFNNs). In the method, each quality characteristic is transformed into a signal-to-noise ratio, and all the ratios are then provided as inputs to a fuzzy model to obtain a single comprehensive output measure (COM). The RBFNNs are used to generate a full factorial design. Finally, the average COM values are calculated for different factor levels, where for each factor, the level that maximizes the COM value is identified as the optimal level. Three case studies are presented for illustrating the method, and for all of them, the proposed method affords the largest total anticipated improvements in multiple quality responses compared with previously used methods, including the fuzzy, grey-Taguchi, Taguchi, and principal component analysis methods. The main advantages of the proposed method are its effectiveness in obtaining global optimal factor levels, its applicability and the requirement of less computational effort, and its efficiency in improving performance. In conclusion, the proposed method may enable practitioners optimize process performance by using multiple quality characteristics.

[1]  John H. Lilly.  Fuzzy Control and Identification: Lilly/Fuzzy Control , 2010 .

[2]  Pedro Paulo Balestrassi,et al.  Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays , 2012, Expert Syst. Appl..

[3]  Kan Wang,et al.  Core axial power shape reconstruction based on radial basis function neural network , 2014 .

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

[5]  Chao-Ton Su,et al.  Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms , 2012, Expert Syst. Appl..

[6]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[7]  Adem Acır,et al.  Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method , 2013 .

[8]  Pedro Pérez-Higueras,et al.  Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks , 2011 .

[9]  Yi-Chin Fang,et al.  Combining Taguchi with fuzzy method on extended optimal design of miniature zoom optics with liquid lens , 2012 .

[10]  Abbas Al-Refaie,et al.  Optimizing tablets’ quality with multiple responses using fuzzy goal programming , 2014 .

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

[12]  Hywel Rhys Thomas,et al.  Optimization of operating parameters of ground source heat pump system for space heating and cooling by Taguchi method and utility concept , 2014 .

[13]  Arindam Majumder,et al.  Multi objective optimization of performance parameters of a single cylinder diesel engine running with hydrogen using a Taguchi-fuzzy based approach , 2013 .

[14]  Abbas Al-Refaie.  Optimizing Multiple Quality Responses in the Taguchi Method Using Fuzzy Goal Programming: Modeling and Applications , 2015, Int. J. Intell. Syst..

[15]  Bo-Ren Hsueh,et al.  Optical design and multi-objective optimization with fuzzy method for miniature zoom optics , 2011 .

[16]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[17]  Ksenija Mandic,et al.  Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods , 2014 .

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

[19]  Toly Chen,et al.  An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication , 2015, Int. J. Intell. Syst..

[20]  Silvia Curteanu,et al.  An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process , 2011, Eng. Appl. Artif. Intell..

[21]  T. Muthuramalingam,et al.  Application of Taguchi-grey multi responses optimization on process parameters in electro erosion , 2014 .

[22]  Antonio Messineo,et al.  Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building , 2014 .

[23]  Alireza Bahadori,et al.  Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure , 2013 .

[24]  Habib Rajabi Mashhadi,et al.  A fast static security assessment method based on radial basis function neural networks using enhanced clustering , 2013 .

[25]  Hari Singh,et al.  Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel , 2011, Expert Syst. Appl..

[26]  D. K. Sahoo,et al.  Application of Taguchi method for optimization of process parameters in decalcification of samarium–cobalt intermetallic powder , 2014 .

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

[28]  吳欣潔,et al.  CART-BPN Approach for Estimating Cycle Time in Wafer Fabrication , 2015 .

[29]  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.

[30]  Javad Zolgharnein,et al.  Multi-response optimization using Taguchi design and principle component analysis for removing binary mixture of alizarin red and alizarin yellow from aqueous solution by nano γ-alumina. , 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[31]  John H. Lilly,et al.  Fuzzy Control and Identification , 2010 .

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

[33]  Majid Moosavi,et al.  Prediction of the specific volume of polymeric systems using the artificial neural network-group contribution method , 2013 .

[34]  Abbas Al-Refaie.  Optimizing the performance of the tapping and stringing process for photovoltaic panel production , 2015 .

[35]  Edwin Lughofer,et al.  Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives , 2013, Eng. Appl. Artif. Intell..

[36]  S. M. Asadzadeh,et al.  A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity , 2011 .

[37]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[38]  Abayomi Moradeyo Otebolaku,et al.  Context-aware media recommendations for smart devices , 2014, J. Ambient Intell. Humaniz. Comput..

[39]  Abbas Al-Refaie.  Applying process analytical technology framework to optimize multiple responses in wastewater treatment process , 2014 .

[40]  Abbas Al-Refaie,et al.  Optimization of multiple responses in the Taguchi method using fuzzy regression , 2014, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[41]  Abbas Al-Refaie,et al.  Optimising performance of low-voltage cables’ process with three quality responses using fuzzy goal programming , 2014 .

[42]  Tsung-Nan Tsai,et al.  Improving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model , 2011 .

[43]  Abbas Al-Refaie,et al.  A proposed satisfaction function model to optimize process performance with multiple quality responses in the Taguchi method , 2014 .