A tool to support designers in comparing the performances of multi-materials with those of other materials and to guide their selection for a given application is discussed. Particulate and fibrous composites can be included in a materials database, allowing direct comparison and selection by established material selection methodologies in the catalogue approach. A common design strategy for multi-materials is to tailor them to fulfill the design requirements as losely as possible by varying either constituents, configuration or scale. The basis for the selection of materials in forming multi-materials is to use the desirable attributes of one monolithic material to compensate for the inadequate attributes of others.@en

In order to fulfill increasingly stringent design criteria, designers may need to consider more advanced engineering materials. One way to improve the performance of materials is to combine different monolithic materials in such a way as to capture the best qualities of both. Such combined, heterogeneous materials are known as multi-materials or hybrid materials. Fiber-reinforced materials and sandwich structures are examples. Selection methodologies and tools for selecting monolithic materials are well established and widely used, but less is known about the design and selection of multi-materials. Here we develop a systematic selection methodology and tool to identify promising solutions, focusing on material selection for foam-core sandwich beams. Two approaches are developed in this study: a real route and a virtual route, pursuing three design objectives: to minimize mass, to minimize cost and to minimize heat transfer. The results of both approaches are compared and discussed. A selection example is also presented to illustrate the use of this tool.

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The selection of a material to meet given design requirements generally requires that a compromise be struck between several, usually conflicting, objectives. The problem is complicated because the decision-space is large, it is discrete rather than continuous, and the relative value to be placed on each objective is imprecisely known. Here we explore the ways in which multi-objective optimisation methods can be adapted to address this problem. We find that trade-off surfaces give a way of visualizing the alternative compromises, and that "utility" functions (or "value" functions) identify the part of the surface on which optimal solutions lie. Implementing this for two objectives is straightforward, but doing so for more then two requires new visualization tools. Here we develop a tool and illustrate its use.@en