The filter membrane made up of carbon nanostructure is one of the important components in proton exchange membrane fuel cell (PEMFC). The membrane while under operating conditions of a PEMFC is subjected to various dynamical loads due to the imposition of several input operating factors of the PEMFC. Hence, it is important to estimate optimal process parameters, which can maximize the strength of the membrane. Current studies in PEMFC focus on adsorption and transport-related properties of PEMFC membrane, without adequately investigating the mechanical strength of the membrane. This study proposes a multiphysics model of the membrane, which is used to extract the mechanical properties of the membrane by systematically varying various input factors of PEMFC. The extracted data are then fed into a neural search machine learning cluster to obtain optimal design parameters for maximizing the strength of the membrane. It is expected that the findings from this study will provide critical design data for manufacturing PEMFC membranes with high strength and durability.

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