Dual Response Approach in Process Capability based on Hybrid Neural Network-Genetic Algorithms

Main Article Content

Tritiya A.R. Arungpadang
Stenly Tangkuman
Lily S. Patras


Process capability has long been recognized as an important performance measure to prove how well the process meets the requirements. Process capability can be improved by applying dual response approach, to determine optimal input factors. Using of artificial intelligence can optimize the prediction of the best input combination with a limited number of experiments. This study proposes an alternatives procedure using a dual response approach and artificial intelligence. One of the most common robust design models has been formulated to minimize variability while maintaining the mean on the desired target. A study case was selected to implement the proposed approach and compare it with conventional optimization models to show the improvement in procedures.


Download data is not yet available.


Metrics Loading ...

Article Details

How to Cite
ArungpadangT. A., TangkumanS., & PatrasL. S. (2019). Dual Response Approach in Process Capability based on Hybrid Neural Network-Genetic Algorithms. Journal of Sustainable Engineering: Proceedings Series, 1(1), 117-122. https://doi.org/10.35793/joseps.v1i1.16


Arungpadang, T.R., & Kim, Y.J. (2012), Robust Parameter Design Based on Back Propagation Neural Network, Management Science (The Korean OR/MS Society), 29(3), pp. 81-89.

Arungpadang, T.R., & Kim, Y.J. (2013a), A Study on Dual Response Approach Combining Neural Network and Genetic Algorithms, Journal of the Korean Institute of Industrial Engineers, 39, pp. 361-366.

Arungpadang, T.R., & Kim, Y.J. (2013b) Design Optimization for Capability Enhancement based on a Dual Response Approach, ICIC Express Letters, 7(5), pp. 1731-1736.

Harrington, E.C. (1965) The Desirability Function, Industrial Quality Control, 21, pp. 494-498.

Kim, Y.J., & Cho, B.R. (2005) Trade-off Strategies in Robust Design Optimization Problems, Journal of the Korean Maintenance Management Society, 10, pp. 69-74.

Kwon, Y.J., Kim, Y.J., & Cha, M.S. (2008) Desirability Function Modeling for Dual Response Surface Approach to Robust Design, Industrial Engineering and Management Systems, 7, pp. 197-203.

Ma, H.Y., & Su, C.T. (2010) Applying Hiera¬rchical Genetic Algorithm Based Neural Network and Multiple Objective Evolutionary Algorithm to Optimize Parameter Design with Dynamic Characteristics, Jour¬nal of Quality, 17 (4), pp. 311-325.

Plante, R.D., (2001) Process Capability: a Criterion for Optimizing Multiple Response Product and Process Design, IIE Transactions, 33, pp. 497-509.

Sexton, R.S., Dorsey, R.E., & Sikander, N.A. (2004) Simultaneous Optimization of Neural Network Function and Architecture Algorithm, Decision Support Systems, 36 (3), pp. 283-296.

Subramanian, N., Yajnik, A., & Murthy, R.S. (2004) Artificial Neural Network as an Alternative to Multiple Regression Analysis in Optimizing Formulation Parameters of Cytarabine Liposomes, AAPS PharmSci Tech, 5(1), E4.

Vining, G.G., & Myers, R.H. (1990) Combining Taguchi and Response Surface Philosophies : A Dual Response Approach, Journal of Quality Technology, 22(1), pp. 38-45.