Download Free Data Mining For Design And Manufacturing Book in PDF and EPUB Free Download. You can read online Data Mining For Design And Manufacturing and write the review.

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.
During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges. The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management.
The Adaptive Computing in Design and Manufacture Conference series is now in its tenth year and has become a well-established, application-oriented meeting recognised by several UK Engineering Institutions and the International Society of Genetic and Evolutionary Computing. The main theme of the conference again relates to the integration of evolutionary and adaptive computing technologies with design and manufacturing processes whilst also taking into account complementary advanced computing technologies. Evolutionary and adaptive computing techniques continue to increase their penetration of industrial and commercial practice as their powerful search, exploration and optimisation capabilities become ever more apparent. The last two years have seen a very significant increase in the development of commercial software tools utilising adaptive computing technologies and the emergence of related commercial research and consultancy organisations supporting the introduction of best practice in terms of industrial utilisation. Adaptive Computing in Design and Manufacture V is comprised of selected papers that cover a diverse set of industrial application areas including: engineering design and design environments, manufacturing process design, scheduling and control, electronic circuit design, fault detection. Various aspects of search and optimisation such as multi-objective and constrained optimisation are also investigated in the context of integration with industrial processes. In addition to evolutionary computing techniques, both neural-net and agent-based technologies play a role in a number of contributions. This collection of papers will be of particular interest to both industrial researchers and practitioners in addition to the academic research communities of engineering, operational research and computer science.
This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research. It describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.
This volume contains the edited technical presentations of PROLMAT 2006, the IFIP TC5 international conference held on June 15-17, 2006 at the Shanghai University in China. The papers collected here concentrate on knowledge strategies in Product Life Cycle and bring together researchers and industrialists with the objective of reaching a mutual understanding of the scientific - industry dichotomy, while facilitating the transfer of core research knowledge to core industrial competencies.
Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: • supply chain design, • product development, • manufacturing system design, • product quality control, and • preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.
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 and professional engineers and managers working in design systems, manufacturing, and related areas.
CUTTING-EDGE CONTENT AND GUIDANCE FROM A DATA WAREHOUSING EXPERT—NOW EXPANDED TO REFLECT FIELD TRENDS Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery. This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information. Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
Organizes major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). This book provides algorithmic descriptions of classic methods, and also suitable for professionals in fields such as computing applications, information systems management, and more.
During the past twenty years, digital design and manufacturing technology has become indispensable in many and various applications world-wide; involving many products and rapidly expanding markets. It has not only provided industry with new methods, tools and digitalized products - from design, materials processing to operating and management procedures - but is also changing the approaches, thinking patterns and working environments of people in the manufacturing field. The rapid growth of digital design and manufacturing processes has also brought with it some processing work-flow challenges. While the various resultant products provide an ideal solution for some processing steps, more dedicated and integrated systems are sometimes required. How best can one handle incoming data and orders, automate the design and perhaps engineering, make robust plans, manage the process and data and deliver quality goods.
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by 'Data Mining' address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as OC enterprise dataOCO. The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making. Sample Chapter(s). Foreword (37 KB). Chapter 1: Enterprise Data Mining: A Review and Research Directions (655 KB). Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers. Readership: Graduate students in engineering, computer science, and business schools; researchers and practioners of data mining with emphazis of enterprise data mining."
Volume is indexed by Thomson Reuters CPCI-S (WoS). This collection of 949 peer-reviewed papers on the Frontiers of Manufacturing and Design Science is intended to promote the development of Manufacturing, Design and Materials Science, the strengthening of international academic cooperation and communications and the exchange of research ideas. This book provides readers with a broad overview of the latest advances in the field of Manufacturing and Design Science.
Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corrective action only after variations occur. Learn how the Stream of Variation (SoV) methodology helps reduce or even eliminate variations throughout the entire MMP in Jianjun Shi's Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. The unified methodology outlined in this book addresses all aspects of variation reduction in a MMP, which consists of state space modeling, design analysis and synthesis, engineering-driven statistical methods for process monitoring and root-cause diagnosis, and quick failure recovery and defect prevention. Coverage falls into five sections, beginning with a review of matrix theory and multivariate statistics followed by variation propagation modeling with applications in assembly and machining processes. The third section focuses on diagnosing the sources of variation while the fourth section explains design methods to reduce variability. The final section assembles advanced SoV-related topics and the integration of quality and reliability. Introducing a powerful and industry-proven method, this book fuses statistical knowledge with the engineering knowledge of product quality and unifies the design of processes and products to achieve more predictable and reliable manufacturing processes.
"This book focuses on the customization of services and communication environments to advance user satisfaction--Provided by publisher.
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject.The first part of the boo
In the industrial design and engineering field, product lifecycle, product development, design process, Design for X, etc., constitute only a small sample of terms related to the generation of quality products. Current best practices cover widely different knowledge domains in trying to exploit them to the best advantage, individually and in synergy. Moreover, standards become increasingly more helpful in interfacing these domains and they are enlarging their coverage by going beyond the single domain boundary to connect closely different aspects of the product lifecycle. The degree of complexity of each domain makes impossible the presence of multipurpose competencies and skills; there is almost always the need for interacting and integrating people and resources in some effective way. These are the best conditions for the birth of theories, methodologies, models, architectures, systems, procedures, algorithms, software packages, etc., in order to help in some way the synergic work of all the actors involved in the product lifecycle. This brief introduction contains all the main themes developed in this book, starting from the analysis of the design and engineering scenarios to arrive at the development and adoption of a framework for product design and process reconfiguration. In fact, the core consists of the description of the Design GuideLines Collaborative Framework (DGLs-CF), a methodological approach that generates a collaborative environment where designers, manufacturers and inspectors can find the right and effective meeting point to share their knowledge and skills in order to contribute to the optimum generation of quality products.
In the latter half of the 20th century, forces have conspired to make the human community, at last, global. The easing of tensions between major nations, the expansion of trade to worldwide markets, widespread travel and cultural exchange, pervasive high-speed communications and automation, the explosion of knowledge, the streamlining of business, and the adoption of flexible methods have changed the face of manufacturing itself, and of research and education in manufacturing. The acceptance of the continuous improvement process as a means for organizations to respond quickly and effectively to swings in the global market has led to the demand for individuals educated in a broad range of cultural, organizational, and technical fields and capable of absorbing and adapting required knowledge and training throughout their careers. No longer will manufacturing research and education focus on an industrial sector or follow a national trend, but rather will aim at enabling international teams of companies to cooperate in rapidly designing, prototyping, and manufacturing products. The successful enterprise of the 21st century will be characterized by an organizational structure that efficiently responds to customer demands and changing global circumstances, a corporate culture that empowers employees at all levels and encourages constant communication among related groups, and a technological infrastructure that fully supports process improvement and integration. In changing itself to keep abreast of the broader transformation in manufacturing, the enterprise must look first at its organization and culture, and thereafter at supporting technologies.

Best Books