🤖 AI Summary
This work addresses the tight coupling between design intent and printer-specific representations in heterogeneous manufacturing, which hinders cross-platform reuse. The authors propose a novel compiler architecture that models fabrication-aware design as a staged, type-directed lowering process, decoupling source design, attribute translation, and backend compilation to enable manufacturing-agnostic expression. Introducing compiler paradigms to heterogeneous manufacturing for the first time, the approach unifies volumetric information—such as material composition, hardness, and color—through implicit geometry and typed spatial attribute fields, automatically generating voxel stacks, G-code, or slicer projects. Experiments demonstrate successful fabrication of complex objects embedding CT data, Shore hardness fields, and full-color fields on both material jetting and extrusion platforms, validating cross-process reusability. The accompanying Python toolkit is publicly released.
📝 Abstract
Computational fabrication workflows increasingly require designers to specify spatially varying behavior, appearance, material composition, and process state. Yet most workflows force these intentions into printer-specific representations, such as material fractions, voxel labels, mesh partitions, or slicer settings. This coupling makes heterogeneous designs difficult to reuse because each backend requires a different realization of the same property. We present a compiler architecture for heterogeneous fabrication that treats design as a staged, typed lowering problem. A source design consists of implicit geometry and named, typed spatial attribute fields over a shared object domain. These attributes may encode measured data, visual appearance, target mechanical behavior, material recipes, process parameters, or other user-specified attributes. Translation models derive compiler-required realization attributes from source or intent attributes, and backend compilers emit machine-facing outputs such as voxel material stacks, process G-code, or configured slicer project files. This separates source modeling, attribute translation, and backend compilation, allowing a design to remain expressed in fabrication-agnostic terms while each toolchain determines how intent becomes executable instructions. We validate the method through fabricated objects and measurements across sampled volumetric data, CT-derived visual and mechanical models, Shore-hardness fields, and full-color fields, implemented via material jetting and material extrusion. These results show that one source design can be lowered into distinct material, process, and slicer representations without rewriting it in printer-specific terms. We provide the representation, translation framework, compiler interface, and workflows as an open-source Python package to accelerate research in heterogeneous computational fabrication.