Designing robust products with multiple quality characteristics
Computers and Operations Research
Neural Networks: Algorithms and Applications
Neural Networks: Algorithms and Applications
A data mining approach to dynamic multiple responses in Taguchi experimental design
Expert Systems with Applications: An International Journal
Optimization of a multi-response problem in Taguchi's dynamic system
Computers and Industrial Engineering
A review of robust optimal design and its application in dynamics
Computers and Structures
A hybrid system for dental milling parameters optimisation
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Journal of Intelligent Manufacturing
Desirability improvement of committee machine to solve multiple response optimization problems
Advances in Artificial Neural Systems
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Engineers have widely applied the Taguchi method, a traditional approach for robust experimental design, to a variety of quality engineering problems for enhancing system robustness. However, the Taguchi method is unable to deal with dynamic multiresponse owing to increasing complexity of the product or design process. Although several alternative approaches have been presented to resolve this problem, they cannot effectively treat situations in which the control factors have continuous values. This study incorporates desirability functions into a hybrid neural network/genetic algorithm approach to optimize the parameter design of dynamic multiresponse with continuous values of parameters. The objective is to find the optimal combination of control factors to simultaneously maximize robustness of each response. The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.