Advanced Multiresponse Process Optimization - An Intelligent and Integrated Approach

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.

List of contents

Introduction.- Review of multiresponse optimisation approaches.- An intelligent, integrated, problem-independent method for multiresponse process optimisation.- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation.- Case studies.- Conclusion.

Summary

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.

Product details

• Authors: Tatjana V. ¿Ibalija, Vidosav D. Majstorovi¿, Vidosav Majstorovic, Vidosav D Majstorovic, Vidosav D. Majstorovic, Vidosav D. Majstorović, Tatjana Sibalija, Tatjana V Sibalija, Tatjana V. Sibalija, Tatjana V. Šibalija
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.01.2015
• EAN: 9783319192543
• ISBN: 978-3-31-919254-3
• No. of pages: 298
• Dimensions: 164 mm x 23 mm x 241 mm
• Weight: 627 g
• Illustrations: XVII, 298 p.
• Subjects: Natural sciences, medicine, IT, technology > Technology > Mechanical engineering, production engineering; Operations Research, B, Robotics, Artificial Intelligence, Automation, engineering, Operations Research/Decision Theory, Operations Research and Decision Theory, Manufactures, Management decision making, Manufacturing, Machines, Tools, Processes, Machines, Tools, Processes, Operational research, Decision Making, Computational Intelligence, Control, Robotics, Automation, Robotics and Automation