A hybrid numerical/machine learning model development to improve the bimetal performance in the electric circuit breakers.

ABSTRACT: Bimetals are widely used as a thermal tripping mechanism inside the miniature circuit breakers (MCBs) products when an overload current passes through the circuit for a certain period. Experimental, numerical, and, recently artificial intelligence methods are widely used in designing electric components. However, developing the bimetal for MCB products somewhat differs from developing other conductor items since they are strongly related to the electrical, mechanical, and thermal performance of the MCB. The conventional experimental and numerical approaches are time-consuming processes that cannot be easily utilized in optimizing the product's performance within the development lead time. In this study, a simple, fast, robust, and accurate novel methodology has been introduced to predict t

SUBMITTER: Mallah AR 

PROVIDER: S-EPMC9613663 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature