Authors: Kusiak
ISBN-13: 9780471348795, ISBN-10: 0471348791
Format: Hardcover
Publisher: Wiley, John & Sons, Incorporated
Date Published: May 2000
Edition: 1st Edition
ANDREW KUSIAK, PhD, a highly respected authority on advanced manufacturing, is Professor of Industrial Engineering at the University of Iowa. He is the author of numerous books, including Concurrent Engineering and Intelligent Design and Manufacturing, both published by Wiley.
Take the next step in Integrated Product and Process Development
This pioneering book is the first to apply state-of-the-art computational intelligence techniques to all phases of manufacturing system design and operations. It equips engineers with a superior array of new tools for optimizing their work in Integrated Product and Process Development.
Drawing on his extensive experience in the field of advanced manufacturing, Andrew Kusiak has masterfully embedded coverage of data mining, expert systems, neural networks, autonomous reasoning techniques, and other computational methods in chapters that cover all key facets of integrated manufacturing system design and operations, including:
* Process planning
* Setup reduction
* Production planning and scheduling
* Kanban systems
* Manufacturing equipment selection
* Group technology
* Facilities and manufacturing cell layout
* Warehouse layout
* Manufacturing system product and component design
* Supplier evaluation
Each chapter includes questions and problems that address key issues on model integration and the use of computational intelligence approaches to solve difficulties across many areas of an enterprise. Examples and case studies from real-world industrial projects illustrate the powerfulapplication potential of the computational techniques.
Comprehensive in scope and flexible in approach, Computational Intelligence in Design and Manufacturing is right in step with the enterprise of the future: extended, virtual, model-driven, knowledge-based, and integrated in time and space. It is essential reading for forward-thinking students andprofessional engineers and managers working in design systems, manufacturing, and related areas.
Kusiak (industrial engineering, U. of Iowa) applies current computational intelligence techniques to all phases of manufacturing system design and operations, providing engineers an array of new tools for optimizing their work in integrated product and process development. He draws on his own extensive experience in advanced manufacturing, and incorporates data mining, expert systems, neural networks, autonomous reasoning techniques, and other computational methods to discuss such facets as process planning, setup reduction, selecting manufacturing equipment, group technology, facilities and manufacturing cell layout, and evaluating suppliers. Annotation c. Book News, Inc., Portland, OR (booknews.com)
1 | Computational Intelligence for Manufacturing | |
2 | Neural Network Applications in Intelligent Manufacturing: An Updated Survey | |
3 | Holonic Metamorphic Architectures for Manufacturing: Identifying Holonic Structures in Multiagent Systems by Fuzzy Modeling | |
4 | Neural Network Applications for Group Technology and Cellular Manufacturing | |
5 | Application of Fuzzy Set Theory in Flexible Manufacturing System Design | |
6 | Genetic Algorithms in Manufacturing System Design | |
7 | Intelligent Design Retrieving Systems Using Neural Networks | |
8 | Soft Computing for Optimal Planning and Sequencing of Parallel Machining Operations | |
9 | Application of Genetic Algorithms and Simulated Annealing in Process Planning Optimization | |
10 | Production Planning and Scheduling Using Genetic Algorithms | |
11 | Neural Network Predictive Process Models: Three Diverse Manufacturing Applications | |
12 | Neural Network Applications to Manufacturing Processes: Monitoring and Control | |
13 | Computational Intelligence in Microelectronics Manufacturing | |
14 | Monitoring and Diagnosing Manufacturing Processes Using Fuzzy Set Theory | |
15 | Fuzzy Neural Network and Wavelet for Tool Condition Monitoring | |
16 | Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations | |
17 | Intelligent Quality Controllers for On-Line Parameter Design | |
18 | A Hybrid Neural Fuzzy System for Statistical Process Control | |
19 | RClass[superscript *]: A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis | |
Index |