Advanced multiresponse process optimisation: an intelligent and integrated approach - Sibalija, Tatjana V.; Majstorović, Vidosav D. (Cham : Springer International Publishing, 2015)
Foreword -- Preface -- Acknowledgments -- Contents -- Abbreviations and Symbols -- 1 Introduction -- Abstract -- 1.1 Process Optimisation Based on Experimental Design -- 1.1.1 Foundations of Taguchi's Method -- 1.1.1.1 Orthogonal Arrays -- 1.1.1.2 Robustness -- 1.1.1.3 Quality Loss Function -- 1.2 The Need for Advanced Multiresponse Process Optimisation in a Modern Industry -- References -- 2 Review of Multiresponse Process Optimisation Methods -- Abstract -- 2.1 Conventional Multiresponse Process Optimisation Approaches Based on Statistical Methods -- 2.1.1 Response Surface Methodology. -- 2.1.2 Taguchi's Robust Parameter Design -- 2.1.2.1 Multiresponse Optimisation Based on Engineering Experience and Knowledge About the Process in Taguchi Method -- 2.1.2.2 Multiresponse Optimisation Based on the Assignment of Weigh Factors to Process Responses in Taguchi Method -- 2.1.2.3 Multiresponse Optimisation Based on Regression Analysis in Taguchi Method -- 2.1.2.4 Multiresponse Optimisation Based on Desirability Function Analysis in Taguchi Method -- 2.1.2.5 Multiresponse Optimisation Based on Data Envelopment Analysis in Taguchi Method. --2.1.2.6 Multiresponse Optimisation Based on Principal Component Analysis in Taguchi Method -- 2.1.2.7 Multiresponse Optimisation Based on Grey Relational Analysis in Taguchi Method -- 2.1.2.8 Other Conventional Multiresponse Optimisation Approaches -- 2.1.3 Multiresponse Optimisation Based on Goal-Programming -- 2.2 Non-conventional Multiresponse Process Optimisation Approaches Based on Artificial Intelligence Techniques -- 2.2.1 Multiresponse Optimisation Based on Fuzzy Multi-attribute Decision Making and Fuzzy Logic -- 2.2.2 Multiresponse Optimisation Based on Artificial Neural Networks. -- 2.2.3 Multiresponse Optimisation Based on Metaheuristic Search Techniques -- 2.2.3.1 Multiresponse Optimisation Based on Genetic Algorithm -- 2.2.3.2 Multiresponse Optimisation Based on Simulated Annealing -- 2.2.3.3 Multiresponse Optimisation Based on Particle Swarm Optimisation -- 2.2.3.4 Multiresponse Optimisation Based on Ant Colony Optimisation -- 2.2.3.5 Multiresponse Optimisation Based on Tabu Search -- 2.2.3.6 Multiresponse Optimisation Based on Recently Developed Evolutionary Algorithms -- Multiresponse Optimisation Based on Artificial Bee Colony Algorithm. -- Multiresponse Optimisation Based on Biogeography-Based Optimisation -- Multiresponse Optimisation Based on Teaching--Learning-Based Optimisation -- 2.2.4 Multiresponse Optimisation Using Expert System -- References -- 3 An Intelligent, Integrated, Problem-Independent Method for Multiresponse Process Optimisation -- Abstract -- 3.1 Method Overview: Intelligent System for Multiresponse Robust Process Design (IS-MR-RPD) Model -- 3.2 Design of Experimental Plan -- 3.2.1 Taguchi's Experimental Design: Orthogonal Arrays -- 3.2.2 Expert System for the Design of Experiment (ES_DoE) in IS-MR-RPD Model. --3.2.2.1 Expert System Shell Java DON.
Keywords: Expert systems (Computer science)