Miniswys is a swiss company developing ultrasonic piezo-electric actuators to achieve precise bidirectional movements, reaching very large strokes with low driving voltage in compact applications. In order to develop such a product, the engineers perform Finite element analysis simulations, to estimate the dynamic behavior of the design, by performing modal analysis. It is used to estimate the resonance frequencies and structural modes of the geometry under various conditions.
However, each application has its own set of requirements, where small design variations can lead to completely different modal behavior. In this context, Miniswys and Neural Concept have been successfully collaborating over the past months, to build a 3D Deep Learning based surrogate model. It allows to get an instantaneous and precise estimation of the dynamic behavior of these actuators, based on geometric and/or boundary conditions variations.
Using Neural Concept Shape, Miniswys is able to explore in a very fast manner many different designs iterations, without the need of going through the full-fledged simulator at every step. Ultimately Miniswys is able to explore extensively the space of designs, to find innovative geometries, outperforming the classic ones, while drastically reducing the costs and time of the research and development phase. After using the tool, Raphaël Hoesli, CTO of Miniswys and directly involved in the project, expressed his satisfaction in the following words:
“Neural Concept Shape enables us to be much more efficient to design products meeting our customers’ requirements. The feedback from our design iterations is so fast that Miniswys’ engineers can see the evolution of the performance quasi instantaneously while changing the design parameters. In other words, slow iterations are replaced by quick predictions which give us the possibility to intuitively improve the performances of our actuators.”
These successful results encouraged Miniswys to continue using Neural Concept Shape to leverage on this surrogate model in shape optimization for piezo actuators.
Neural Concept Shape is a high-end deep learning software, which understands 3D shapes (CAD), and learns how they interact with the laws of physics (CAE). It is able to emulate full-fledged simulators, giving predictions in approximately 30 ms versus minutes to hours (or even days) for classic simulators. In other terms, engineers can use Neural Concept Shape to explore, manually or automatically, an infinite amount of designs without calling back the resource-consuming, time-consuming simulator. This allows to dramatically accelerate R&D cycles, enhance product performances, and solve the most difficult engineering challenges.