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International journal for simulation and multidisciplinary design optimization, 2024-01, Vol.15, p.5
2024

Details

Autor(en) / Beteiligte
Titel
Enterprise intelligent manufacturing data analysis technology based on big data analysis
Ist Teil von
  • International journal for simulation and multidisciplinary design optimization, 2024-01, Vol.15, p.5
Ort / Verlag
Les Ulis: EDP Sciences
Erscheinungsjahr
2024
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • The rise of big data has deeply influenced various industries, especially the intelligent manufacturing of enterprises. However, traditional data analysis methods are difficult to adapt to the storage and analysis of sea volume data in intelligent production. To address this issue, a method relying on big data analysis and cluster analysis is proposed to design data analysis techniques for enterprise intelligent manufacturing. The proposed improved algorithm is subjected to performance testing. The accuracy of this algorithm is 97%, which exceeds the comparison algorithm. The error is 6% and the running time is 5 s, both of which are below the comparison algorithm. The effectiveness of the enterprise intelligent manufacturing data analysis technology is tested. The experimental group completes orders in 4.1 weeks, 5.2 weeks, 3 weeks, 3.4 weeks, and 4.9 weeks, respectively, shorter than the control group. The product qualification rates for the experimental group are 92%, 93%, 95%, 92%, and 92%, respectively, which exceed the control group. In summary, the proposed enterprise intelligent manufacturing data analysis technology relying on big data and cluster analysis can better utilize data resources and information technology, improving the production efficiency and competitiveness of enterprises. It is hope that this research result can provide useful guidance and reference for the application and development of intelligent manufacturing data analysis technology in enterprises.
Sprache
Englisch
Identifikatoren
ISSN: 1779-6288
eISSN: 1779-6288
DOI: 10.1051/smdo/2024005
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_af66d1714335432ebb0fc274a79d474c

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