Visualized CAD models of objects made of a multiphase perfect material

A component, which has a perfect combination of different materials (including homogeneous materials and different types of heterogeneous materials) in its different portions for a specific application, is considered as the component made of a multiphase perfect material. After design of such a component according to the requirements from a specific application, a CAD model for representing it should be built so that its further analysis and manufacturing can be implemented based on the model. According to a new modeling method, this paper further presents the approaches of retrieving and visualizing all the necessary information of each layer of a component from its CAD model. The information includes: (1) 2D geometric boundary of each material region on each layer; (2) volume fraction of each material constituent for each position in each material region; and (3) 2D geometric boundaries of void phases of base cells in material regions with a periodic microstructure. Finally, two examples are given to demonstrate both geometric and material information on the layers, thus verifying the proposed approach. © 2005 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

Optimal Design of Functionally Graded Parts

Several additive manufacturing processes are capable of fabricating three-dimensional parts with complex distribution of material composition to achieve desired local properties and functions. This unique advantage could be exploited by developing and implementing methodologies capable of optimizing the distribution of material composition for one-, two-, and three-dimensional parts. This paper is the first effort to review the research works on developing these methods. The underlying components (i.e., building blocks) in all of these methods include the homogenization approach, material representation technique, finite element analysis approach, and the choice of optimization algorithm. The overall performance of each method mainly depends on these components and how they work together. For instance, if a simple one-dimensional analytical equation is used to represent the material composition distribution, the finite element analysis and optimization would be straightforward, but it does not have the versatility of a method which uses an advanced representation technique. In this paper, evolution of these methods is followed; noteworthy homogenization approaches, representation techniques, finite element analysis approaches, and optimization algorithms used/developed in these studies are described; and most powerful design methods are identified, explained, and compared against each other. Also, manufacturing techniques, capable of producing functionally graded materials with complex material distribution, are reviewed; and future research directions are discussed

Bi-directional evolutionary structural optimization (BESO) for topology optimization of material’s microstructure

It is known that composite materials with improved properties can be achieved through modifications to the topology of their microstructures. Structural topology optimization approaches can be utilized as a systematic way for finding the best spatial distribution of constituent phases within the microstructures of materials/composites. This study presents a new approach for designing material’s microstructures based on the Bi-directional Evolutionary Structural Optimization (BESO) methodology. It is assumed that the materials/composites are composed of repeating microstructures known as periodic base cells (PBC). The goal is to find the best spatial distribution of constituent phases within the PBC, in such a way that materials with desired or improved functional properties are achieved. To this end, the Homogenization theory is applied to establish a relationship between properties of materials microstructure and their macroscopic characteristics. As the first step of this study, the optimization problem is formulated to find microstructures for materials with maximum stiffness, in the form of bulk or shear modulus, or thermal conductivity. Cellular materials, which are composed of one solid phase and one void phase, are considered at this stage. By conducting finite element analysis of the PBC, and applying the Homogenization theory, elemental sensitivity numbers are derived. By gradual removing and adding elements in an iterative process, the optimal topology of the solid phase within the PBC is found. In the next stage of this study, the aim is to combine additional performance constraint to the above procedure. Maximization of bulk or shear modulus is selected as the objective of the material design, subject to the constraint on the isotropy of material and volume constraint. The methodology is extended into topology optimization of microstructures for composites of two or more non-zero constituent phases. For design of material with maximum stiffness or thermal conductivity, the constituent phases are divided into groups and sensitivity analysis is performed between different groups. The developed methodology is also applied in designing functionally graded material (FGM), in which the mechanical property of material gradually changes. It is assumed that the microstructure of the FGM is composed of a series of cellular base cells in the direction of gradation and self-repeated in other directions. Finally, an approach is proposed for the topological design of FGMs with two non-zero constituent phases and multi graded properties. The objective of optimization is defined to find the stiffest materials with prescribed gradation of thermal conductivity. Similar to the approach used for cellular FGMs, the connectivity of base cells is maintained by considering three base cells at each stage. The effectiveness and computational efficiency of the proposed approaches are numerically tested, through designing a range of 2D and 3D microstructures for materials. A series of new and interesting microstructures of materials are presented. The results clearly indicate the advantages of BESO utilization in terms of computational costs and convergence speed, quality of generated microstructures, and ease of implementation as a post processing algorithm