  To create heterogeneous, multiscale structures with unprecedented
functionalities, recent topology optimization approaches design either fully
aperiodic systems or functionally graded structures, which compete in terms of
design freedom and efficiency. We propose to inherit the advantages of both
through a data-driven framework for multiclass functionally graded structures
that mixes several families, i.e., classes, of microstructure topologies to
create spatially-varying designs with guaranteed feasibility. The key is a new
multiclass shape blending scheme that generates smoothly graded microstructures
without requiring compatible classes or connectivity and feasibility
constraints. 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 and
diversity-based freeform topologies demonstrate the versatility of our
framework, while studies on the effect of the number and diversity of classes
illustrate the effectiveness. The generality of the proposed methods supports
future extensions beyond the linear applications presented.
