E-Drive Housing Designs Optimized using Neural Concept Shape
Neural Concept and Bosch Research collaborated over the past months on a set of successful applications of 3D geometric deep learning techniques powered by Neural Concept Shape (NCS) software.
More particularly, we achieved promising results on E-Drive motor housing simulations. Bosch Research engineers trained a deep Geometric Convolutional Neural Network (GCNN) to emulate accurately, in a few ms, the fully fledged Finite Element software.
These successful results encouraged Bosch Research to continue the collaboration with Neural Concept on a further application of shape design optimization.
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 30ms, 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 accelerated R&D cycles, enhance product performances and solve the most difficult engineering challenges.
"For the considered application, NCS performs clearly better than currently used surrogate models and therefore we see the potential of NCS for more use-cases."
Roland Schirrmacher
Structural Dynamics and Acoustics engineer at Bosch
