# A Case Study of Applying Generative Design to Gear Wheels

## TL;DR

This study explores how generative design can optimize gear wheel shapes while considering manufacturing methods, achieving significant weight reductions.

## Contribution

The paper introduces a finite element-based workflow for generative design of gears, showing how manufacturing constraints affect design outcomes.

## Key findings

- Generative design reduced gear-body mass by up to 37.46–45.68% compared to traditional designs.
- Additive manufacturing constraints led to the highest weight savings, while machining constraints improved structural stiffness.
- Casting constraints produced more conservative designs with reinforced areas.

## Abstract

This paper presents a numerical case study on the application of generative design to the shape optimisation of a spur gear body with consideration of manufacturing constraints and material properties. The presented results are obtained using numerical simulation and finite element analysis. A finite element-based generative design workflow was employed to evaluate weight reduction and stiffness performance under different manufacturing routes, including additive manufacturing, machining, and casting. The results show that the application of generative design enabled a gear-body mass reduction of up to 37.46–45.68% compared to the reference geometry while maintaining acceptable deformation levels. Designs constrained for additive manufacturing achieved the highest weight savings, whereas machining-constrained variants exhibited lower deformation values, indicating higher structural stiffness. Casting-oriented constraints resulted in more conservative geometries with locally reinforced regions. The study confirms that manufacturing constraints significantly influence the generated geometry and mechanical response, demonstrating that the manufacturing route acts as an independent design variable within the generative design process. The presented methodology provides practical guidance for the early-stage numerical optimisation of gear bodies and supports informed decision-making with respect to manufacturing technology selection.