Publisher's Synopsis
Topology optimization is a tool for finding a domain in which material is placed that optimizes a certain objective function subject to constraints. By performing a shape optimization on a structure, its shape in terms of thickness and radius is varied where non-linear and fatigue material behaviour can be taken into account. As the need to cut lead times in the product development process as well as the need to reduce weight of automotive vehicles increases, it becomes more natural to include topology and shape optimization in early phases of the component development process. Topology optimization (TO) is the most general type of structural optimization, being performed in the initial phases of the design. Topology optimization is responsible with most of the objective satisfaction (about 70% of the final design objective), offering an initial model that can be fine-tuned afterwards with shape and size optimization methods. This volume presents compilation of the issues and opportunities for the application of topology optimization methods for additive manufacturing (AM). The volume presents latest developments and applications in this increasingly popular & maturing optimization approach for engineers and architects. The area of topology optimization of continuum structures of which is allowed to change in order to improve the performance is now dominated by methods that employ the material distribution concept. An urgent and realistic need in designing structures, e.g., car bodies, is to find an optimal design for minimizing vibration and noise, maximizing safety, minimizing the cost of products, etc. The need is constantly enforced in the process of contemporary commodity competition. Thus, structural optimization techniques have been developed rapidly to deal with these issues in recent years. The interest of engineers and industry supported the development of topology optimization significantly. The volume is intended for professionals who are interested in using the tools provided, but does not require in-depth theoretical knowledge of the subject.