Integrated topology and shape optimization of a thin-walled automotive demonstrator under casting constraints
Lightweight cast automotive components are essential for reducing CO₂ emissions while ensuring sufficient structural performance. Sand casting of nodular cast iron remains a cost-efficient method for producing complex geometries; however, thin-walled sections are still challenging due to risks of incomplete filling, core-removal constraints, and defects associated with rapid solidification. This study introduces an integrated workflow that links topology optimization, casting-aware redesign, and shape optimization to develop a thin-walled demonstrator for a trailer component. Beginning with a simplified design space, topology optimization was used to establish an efficient load-carrying design. The geometry was then reworked to fulfill key casting constraints, including minimum wall thickness and core removability. A subsequent shape optimization step refined the hole regions to reduce local stress concentrations, while casting simulations were employed to assess manufacturability and identify potential defect-prone zones. The proposed workflow provides a manufacturing-oriented approach for converting topology-optimized designs into castable thin-walled components under realistic process constraints. By combining structural optimization with process-driven design adaptations, it delivers a lightweight and mechanically robust design that was experimentally validated through successful sand casting of the demonstrator.
Authors
- Lukas Kettenhofen
- Nima Roudbarian
- Mohammadali Ebadi
- Mahan Firoozbakht
- Andreas Bührig-Polaczek
- Kai-Uwe Schröder
- Jayesh Singh
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- Journal of Manufacturing Processes
- Published
- 2026-09-04
- DOI
- https://doi.org/10.1016/j.jmapro.2026.09.004
- Primary Topic
- Topology Optimization in Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bundesministerium für Wirtschaft und Energie
- RWTH Aachen University