Computer-based methods such as topology optimization offer high potential to improve the performance and resource efficiency of structural components while reducing product development time. Beyond end-use components, significant opportunities exist in the optimization of forming tools, which shape sheet metal parts into their intended geometry. Achieving high manufacturing accuracy requires a uniform pressure distribution during the forming process, which can be supported by topology optimization through the design of optimal rib structures. Forming tools are typically manufactured using the lost-foam casting process, where a Styrofoam model is milled, embedded in sand, vaporized and replaced by molten metal during casting. However, the Styrofoam model is subjected to highly inconsistent loads during sand compaction, which cannot be modelled as well-defined load cases in simulation. Excessive deformation due to the sand pressure leads to geometric deviations that may render the final tool unusable. Ensuring robustness against these uncertainties is therefore critical. This talk presents a topology optimization approach for forming tools that (i) restricts the internal stiffening ribs to an extruded profile to control manufacturability, and (ii) enforces a connected profile to avoid freestanding features prone to instability during sand compaction. Connectivity is ensured using an eigenvalue-based constraint derived from the Dirichlet–Laplacian problem, following Donoso et al. [1][2]. By requiring the first eigenvalue to exceed a threshold, disconnected or weakly connected regions are penalized, guaranteeing a single, coherent rib network. This geometric constraint provides structural integrity without modelling uncertain compaction loads explicitly, thereby achieving robustness with minimal computational effort. The approach is demonstrated on three-dimensional examples of varying complexity using the three-field topology optimization scheme. Results show that the proposed extruded-and-connected profile formulation substantially enhances robustness to unpredictable compaction loads, supporting reliable, accurate, and efficient forming-tool design.