Riassunto
Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making.
Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.
Contenuti
Preface. Data Mining for Design and Manufacturing; D. Braha. Part I: Overview of Data Mining. 1. Data Mining: An Introduction; I.K. Sethi. 2. A Survey of Methodologies and Techniques for Data Mining and Intelligent Data Discovery; R. Gonzalez, A. Kamrani. Part II: Data Mining in Product Design. 3. Data Mining in Scientific Data; S. Rudolph, P. Hertkorn. 4. Learning to Set Up Numerical Optimizations of Engineering Designs; M. Schwabacher, et al. 5. Automatic Classification and Creation of Classification Systems Using Methodologies of `Knowledge Discovery in Databases (KDD)'; H. Grabowski, et al. 6. Data Mining for Knowledge Acquisition in Engineering Design; Y. Ishino, Y. Jin. 7. A Data Mining-Based Engineering Design Support Systema: A Research Agenda; C.J. Romanowski, R. Nagi. Part III: Data Mining in Manufacturing. 8. Data Mining for High Quality and Quick Response Manufacturing; J.-H. Lee, S.-C. Park. 9. Data Mining for Process and Quality Control in the Semiconductor Industry; M. Last, A. Kandel. 10. Analyzing Maintenance Dance Using Data Mining Methods; C.J. Romanowski, R. Nagi. 11. Methodology of Mining Massive Data Sets for Improving Manufacturing Quality/Efficiency; J.-C. (JC) Lu. 12. Intelligent Process Control System for Quality Improvement by Data Mining in the Process Industry; S. Oh, et al. 13. Data Mining by Attribute Decompositon with Semiconductor Manufacturing Case Study; O. Maimon, L.S. Rokach. 14. Derivation of Decision Rules for the Evaluation ofProduct Performance Using Genetic Algorithms and Rough Set Theory; Z. Lian-Yin, et al. 15. An Evaluation of Sampling Methods for Data Mining with Fuzzy C-Means; K. Josien, et al. 16. Colour Space Mining for Industrial Monitoring; K.J. Brazier, et al. 17. Non-Traditional Applications of Data Mining; A. Kusiak. 18. Fuzzy-Neural-Genetic Layered Multi-Agent Reactive Control of Robotic Soccer; A.V. Topalov, S.G. Tzafestas. Part IV: Enabling Technologies for Data Mining in Design and Manufacturing. 19. Method-Specific Knowledge Compilation; J.W. Murdock, et al. 20. A Study of Technical Challenges in Relocation of a Manufacturing Site; G. Zhang, S. Athalye. 21. Using Imprecise Analogical Reasoning to Refine the Query Answers for Heterogeneous Multidatabase Systems in Virtual Enterprises; Z.M. Ma, et al. 22. The Use of Process Capability Data in Design; A. Thornton. Index.
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