Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation pr...

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Detalles Bibliográficos
Autores principales: Qiu, Hao, Feng, Yixiong, Hong, Zhaoxi, Li, Kangjie, Tan, Jianrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232523/
https://www.ncbi.nlm.nih.gov/pubmed/35750710
http://dx.doi.org/10.1038/s41598-022-14835-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232523/
https://www.ncbi.nlm.nih.gov/pubmed/35750710
http://dx.doi.org/10.1038/s41598-022-14835-1

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