Тип публикации: статья из журнала
Год издания: 2021
Идентификатор DOI: 10.1109/ACCESS.2021.3092221
Ключевые слова: welding, prediction algorithms, electron beams, predictive models, correlation, random forests, testing, electron beam welding, choice of process parameters, software, decision support, prediction, ridge regression, random forest, genetic algorithm, algor, algorithm ensembles, machine learning
Аннотация: This paper discusses the problem of choosing the effective process parameters of electron beam welding (EBW). To that end, the research team has developed a mathematical model that applies machine learning to predict the effective process parameters. Sinc
Журнал: IEEE ACCESS
Выпуск журнала: Vol. 9
Номера страниц: 92483-92499
ISSN журнала: 21693536
Место издания: PISCATAWAY
Издатель: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC