Making Helmets Safer with Generative Design
Over the last several decades, generative design techniques have enabled designers and engineers to broaden their exploration of topology and performance of human-scale structural forms in Architecture. Autodesk is collaborating with Lawrence Livermore National Labs to extend this exploration to micro-architecture and how to design materials at the microscopic level. The researchers intend to generate and analyze the performance of very large sets – thousands to tens of thousands – of different structural configurations of material microarchitectures using generative (aka computational) techniques. Helmet design is an excellent example of a multi-objective design problem where constraining for weight, cost, durability, material thickness, and response to compression and sheer within the range of impact conditions will produce multiple high-performing material configurations.
Likewise, helmet design stands to advance considerably from additive manufacturing. The internal structures of helmets not only need to be lightweight, but also must absorb impact and dissipate energy predictably. Advanced additive manufacturing techniques can produce complex material microstructures that will dissipate energy more predictably and repeatedly than what is currently possible with traditionally manufactured helmet pads such as foams and gels. When paired with advanced computational design methods, additive manufacturing opens up the opportunity for a functionally graded multi-material design that integrates the helmet shell with its cushioning element. A fully validated, 100 percent additive helmet is an audacious goal, yet this collaboration expects demonstrable progress toward a prototype.
Erin Bradner from the Dreamcatcher team explains more about this exciting project in the following video from Wired exploring the future of football and dealing with concussions.
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