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To create heterogeneous, multiscale structures with unprecedentedfunctionalities, recent topology optimization approaches design either fullyaperiodic systems or functionally graded structures, which compete in terms ofdesign freedom and efficiency. We propose to inherit the advantages of boththrough a data-driven framework for multiclass functionally graded structuresthat mixes several families, i.e., classes, of microstructure topologies tocreate spatially-varying designs with guaranteed feasibility. The key is a newmulticlass shape blending scheme that generates smoothly graded microstructureswithout requiring compatible classes or connectivity and feasibilityconstraints. Moreover, it transforms the microscale problem into an efficient,low-dimensional one without confining the design to predefined shapes.Compliance and shape matching examples using common truss geometries anddiversity-based freeform topologies demonstrate the versatility of ourframework, while studies on the effect of the number and diversity of classesillustrate the effectiveness. The generality of the proposed methods supportsfuture extensions beyond the linear applications presented.

Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending