A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys.

ABSTRACT: As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosi

SUBMITTER: Wang X 

PROVIDER: S-EPMC9867464 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature