Machine learning guided development and testing of optimal combat helmet padding concepts for improved blunt impact performance helmets

Researchers: George Barlow, Kayla Murphy, Jay Pandya

Sponsor: US Army Combat Capabilities Development Command Soldier Center

Description: Designing helmet systems to provide better protection against brain injuries caused by blunt impact is an important but difficult task. For this project in collaboration with the US Army Combat Capabilities Development Command Soldier Center, a computer based (LS DYNA finite element) model has been created that allows for the performance of the helmet using different foam pads and configurations to be tested at different impact velocities and locations on the helmet shell. The creation and processing of these models is accomplished using an automation scheme utilizing MATLAB to control SOLIDWORKS and HyperMesh to create the geometry for the model. The outputs of these models are shared with a data analytics team that using machine learning to predict where in the design space to concentrate further sampling in the search of a more optimum design. The optimum designs found through the sampling and machine learning process are then planned to be manufactured to be experimentally tested by Gentex Corporation.