Uncertainties play a dominant role in the design and optimization of structures and infrastructures. In optimum design of structural systems due to variations of the material, manufacturing variations, variations of the external loads and modelling uncertainty, the parameters of a structure, a structural system and its environment are not given, fi

Collection of selected, peer reviewed papers from the 2013 2nd International Conference on Machine Design and Manufacturing Engineering (ICMDME 2013), May 1-2, 2013, Jeju Island, South Korea. Volume is indexed by Thomson Reuters CPCI-S (WoS). The 275 papers are grouped as follows: Chapter 1: Design of Machines, Mechanisms and Industrial Devices; Chapter 2: Computational Technologies and Computer-Aided Design in Mechanical Engineering; Chapter 3: Researches, Modeling and Analysis of Machines and Mechanisms; Chapter 4: Automotive Engineering; Chapter 5: Technologies and Organization of Production in Mechanical Engineering; Chapter 6: Sensors, Detection and Measuring Technologies; Chapter 7: Robotics, Automation and Control System; Chapter 8: Applied Materials Science and Chemical Engineering; Chapter 9: Product Design; Chapter 10: Other Themes of Research.

Engineering Design Optimization is written for students who are looking to optimize their engineering designs, but are unaware of the mathematical rigor needed to address their objectives. This book addresses teaches the algorithms that are used in engineering optimization. Contains unique material on monotonicity, probabalistic design optimization, and genetic algorithms. Keeps mathematics simple, but proves theories as needed. Provides algorithms essential for optimization and encourages students to write their own computer programs.

The objective of controlling uncertainty in mechanical engineering is significantly to enhance safety, reliability and economic efficiency in the development, production, use and conservation of natural resources. Volume is indexed by Thomson Reuters CPCI-S (WoS). This special collection of 19 peer-reviewed papers offers an academic and industrial perspective on the description, evaluation and control of uncertainty in: 1 Development, 2 Production and 3 Usage. This work should meet the needs of a broad readership, and motivate further investigations.

Uncertainties play a dominant role in the design and optimization of structures and infrastructures. In optimum design of structural systems due to variations of the material, manufacturing variations, variations of the external loads and modelling uncertainty, the parameters of a structure, a structural system and its environment are not given, fixed coefficients, but random variables with a certain probability distribution. The increasing necessity to solve complex problems in Structural Optimization, Structural Reliability and Probabilistic Mechanics, requires the development of new ideas, innovative methods and numerical tools for providing accurate numerical solutions in affordable computing times. This book presents the latest findings on structural optimization considering uncertainties. It contains selected contributions dealing with the use of probabilistic methods for the optimal design of different types of structures and various considerations of uncertainties. The first part is focused on reliability-based design optimization and the second part on robust design optimization. Comprising twenty-one, self-contained chapters by prominent authors in the field, it forms a complete collection of state-of-the-art theoretical advances and applications in the fields of structural optimization, structural reliability, and probabilistic computational mechanics. It is recommended to researchers, engineers, and students in civil, mechanical, naval and aerospace engineering and to professionals working on complicated costs-effective design problems.

Containing the proceedings of the 5th International Conference on Computer Aided Optimum Design of Structures, this volume looks at recent advances in structural optimization and demonstrates how optimization can best be applied to engineering practice.