SheetGen: A Rule-Based Generative Design Algorithm for Manufacturability-Focused Sheet Metal Parts Based on Domain-Specific Design Knowledge

Generative design can accelerate early exploration of design solutions, yet generated designs often fail to meet manufacturing requirements. This shortcoming stems from a disconnect between the generation process of the solutions and the domain-specific design knowledge that experienced engineers apply to ensure manufacturability. To address this gap, this article presents SheetGen, a rule-based generative design algorithm for sheet metal bending parts that systematically translate domain-specific design knowledge into algorithmic rules enforced directly during part generation rather than through post hoc filtering. The algorithm operates on edge connections between planar surfaces and embeds manufacturing requirements at distinct stages of the generative design process, distinguishing between preprocessing, in-process, and postprocessing integration points. Generated solutions are exported directly into a commercial computer-aided design (CAD) environment and validated through industrial computer-aided manufacturing (CAM) simulation. SheetGen is evaluated across three test cases. Compared to a baseline algorithm without rule implementation, it raises manufacturability from 40% to 64%. For an input where a generative approach from the literature produces no manufacturable parts, SheetGen achieves 86%. In a plausibility comparison with human designers who achieved 71% manufacturability, SheetGen reaches 96%. The results demonstrate that systematic knowledge integration can substantially improve the manufacturability of generatively designed sheet metal parts, and the identified strengths and limitations of the rule-based approach point toward hybrid strategies that combine rule-based reliability with artificial intelligence (AI)-driven adaptability to further advance generative design for manufacturing.