Design reuse in product development modeling, analysis and optimization / S.K. Ong, Q.L. Xu, Andrew Y.C. Nee.

Includes bibliographical references (pages 273-291) and index.

Print version record.

Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Design Reuse What and Why -- 1.1.1 Types of design reuse -- 1.1.2 The importance of design reuse -- 1.2 Product Conceptual Design -- 1.2.1 Product family design -- 1.3 Major Issues in Design Reuse -- 1.3.1 Design reuse process -- 1.3.2 Product information modeling -- 1.3.3 Product information analysis -- 1.3.4 Design synthesis -- 1.3.5 Solution evaluation -- 1.4 Engineering Design Reuse Applications -- 1.4.1 Design reuse in software engineering -- 1.4.2 Design reuse in mechanical and electro-mechanical engineering -- 1.4.3 Design reuse in manufacturing -- 1.5 Barriers to Design Reuse -- 1.6 Summary -- 2 Design Reuse Systems and Enabling Tools -- 2.1 Engineering Design Reuse Approaches -- 2.1.1 Case-based reasoning -- 2.1.2 Catalog-based design -- 2.1.3 Modular design -- 2.1.4 Adaptable design -- 2.1.5 Expert systems -- 2.1.6 Innovative design using TRIZ -- 2.2 Reasoning in Design Reuse -- 2.2.1 Machine learning -- 2.2.2 Data mining -- 2.2.3 Design structure matrix -- 2.2.4 Artificial neural networks -- 2.2.5 Genetic algorithms -- 2.2.6 Agent-based method -- 2.3 Summary -- 3 Product Information Modeling -- 3.1 Data, Information and Knowledge -- 3.2 Information Modeling State-Of-The-Art Review -- 3.2.1 Content of information model -- 3.2.2 Modeling languages -- 3.2.3 Taxonomies -- 3.2.4 Database system and web-based environment -- 3.3 Function-Based Product Information Model -- 3.3.1 A multiple facet product information model -- 3.3.2 Representation of function using key element vector -- 3.3.3 Function taxonomies -- 3.3.4 An illustrative example -- 3.4 Summary -- 4 Design of Product Platform -- 4.1 Role of Product Platform -- 4.2 Product Platform and Product Family Design -- 4.2.1 A top-down perspective -- 4.2.2 A bottom-up perspective -- 4.3 Computational Tools for Product Architecture Building -- 4.3.1 QFD-based approach -- 4.3.2 DSM-based approach -- 4.3.3 Heuristic and quantitative approaches -- 4.4 Product Architecture Building Using Self-Organizing Map -- 4.4.1 Introduction of SOM -- 4.4.2 Function clustering based on SOM -- 4.4.3 A case study -- 4.4.4 Evaluation of the SOM method -- 4.5 Other Relevant Issues in Product Platform Design -- 4.5.1 Extraction of KCs as performance criteria -- 4.5.2 Formation of component catalog -- 4.5.3 Establishment of mapping route using correlation matrices -- 4.6 Summary -- 5 Optimization in Product Design -- 5.1 Introduction -- 5.1.1 Weighted sum method -- 5.1.2 Goal programming -- 5.1.3 Multi-level programming/rank ordering -- 5.1.4 Genetic algorithms -- 5.2 Automated Design Synthesis -- 5.2.1 Configuration design -- 5.2.2 Design synthesis techniques -- 5.3 Multi-objective Struggle Genetic Algorithm Design Synthesis -- 5.3.1 Problem formulation -- 5.3.2 The MOSGA algorithm -- 5.3.3 Implementation of MOSGA in product configuration design -- 5.3.4 Precautions and limitations -- 5.4 Post-optimal Solution Selection -- 5.5 A Case Study -- 5.5.1 Experience-based design -- 5.5.2 Product design using the design reuse approach -- 5.5.3 Comparison of the two methods -- 5.6 Summary -- 6 Cost Estimation in Product Development -- 6.1 Introduction -- 6.2 Product Development Cost -- 6.2.1 Cost stru.

Efficient management of product information is vital for manufacturing enterprises in this information age. Considering the proliferation of product information, tight production schedules, and intense market competition, human intelligence alone cannot meet the requirements of efficient product development. Technologies and tools that support information management are urgently needed. This volume presents the design reuse methodology to support product development. Significant efforts have been made to create an intelligent and optimal design environment by incorporating the contemporary technologies in product family design, artificial intelligence, neural networks, information theories, etc. This volume covers both theoretical topics and implementation strategies, with detailed case studies to help readers gain an insight in areas such as product information modeling, information analysis, engineering optimization, production cost estimation, and product performance evaluation.

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