Advanced Multiresponse Process Optimisation
An Intelligent and Integrated Approach
Nonfiction, Science & Nature, Technology, Manufacturing, Computers, Advanced Computing, Artificial Intelligence
| Author: | Tatjana V. Šibalija, Vidosav D. Majstorović | ISBN: | 9783319192550 |
| Publisher: | Springer International Publishing | Publication: | July 25, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | Tatjana V. Šibalija, Vidosav D. Majstorović |
| ISBN: | 9783319192550 |
| Publisher: | Springer International Publishing |
| Publication: | July 25, 2015 |
| Imprint: | Springer |
| Language: | English |
This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi’s quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.
This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi’s quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.
