This work pertains to the multi-objective inverse design of impact-resistant metamaterials under varying strain rates. Impact-resistant materials are desirable in a wide range of applications, such as sports, automobiles, military, and aircraft, to name a few. Existing literature deals with refining these structures by performing quasi-static finite element (FE) simulations and then verifying them experimentally, which is a time-consuming and expensive process. Moreover, beyond the low-velocity regime, quasi-static simulations are not representative of real-world dynamic conditions and do not guarantee a similar level of performance. To face these challenges, we propose a multi-objective inverse design approach by combining dynamic impact simulations and machine learning (ML) to overcome the typical trade-off between absorbed energy and transmitted force.
Project is currently funded by: Federal