There are strong correlations between material assignment, shape, and functionality of a part in an overall product/assembly. However, these strong correlations are rarely exploited for automated material assignment. We present a probabilistic graphical model to assign materials to the parts (components) of a 3D object (assembly) by identifying the relations between shape, functionality, and material of the parts. By learning the context-dependent correlation with supervision from a set of objects and their segmented parts, the learned model can be used to assign engineering materials to the parts of a query object. Our primary contributions are (a) the engineering materials definition and assignment and (b) assigning engineering materials based on the behavior and form of the parts in the object. The performance of the proposed computational approach is demonstrated by the results of material assignment on various query objects with prespecified engineering performance requirements.

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