OptiBike robust lightweight composite bicycle design and optimization

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Composite structures are complex and time-consuming to develop. The OptiBike experiment offers a user-friendly service for SMEs to optimize composite material configurations, leveraging machine learning algorithms and cloud-HPC infrastructure, and analyzing manufacturing tolerance uncertainty.

Start date: 01/11/2015

Duration in months: 18

Problem Description

Composite structures, and especially those incorporating carbon fibres, are much more complex than isotropic metal alloys, as they require lengthy development, significant knowledge and fine tuning. Building prototypes to test various configurations may take years to reach an optimal structure.


New services


Numerical simulations can significantly reduce the time and effort required, but modelling software tools and HPC infrastructures represent a large investment and solving optimization problems with hundreds of parameters require highly skilled engineers.

Innovation results

OptiBike offers a user-friendly service for SMEs to optimize composite material layer configurations, utilizing machine learning algorithms to improve performance and manufacturability. The service uses cloud-HPC infrastructure and simulates stresses and deformations on a composite bike frame.

Business impact

The solution reduces IDEC's bicycle design and optimization time by 80% and decreases manufactured prototypes by 75%. Noesis offers composite optimization services, targeting high-end composite parts. The Fortissimo Marketplace will reach new customers and save €45k per bike frame model.

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