Autors: Ivanova, M. S., Tsenev, V. P., Dimitrov B.
Title: Manufacturing Process Optimization Through Machine Learning and Analytical Prediction
Keywords: big data, deep learning, FMEA, machine learning

Abstract: A "smart" production is characterized with collection of a large amount of data and the application of machine and deep learning algorithms for the purposes of analytical prediction. The analysis supports the implementation of intelligent management and rapid response to changes in a manufacturing process. The paper proposes an approach for optimizing a robotic manufacturing line for electronic components through applying the failure mode and effect analysis and algorithm for deep learning. This approach is embedded in a software tool created through C#, Windows Forms technology and open source to assist identification of the potential risks by the responsible engineer.

References

    Issue

    International Conference Statistics and Machine Learning in Electronics/Complex Control Systems, vol. 4, issue 1, pp. 30-35, 2022, Bulgaria, IR-BAS, ISBN ISSN 2603-4697 (Online)

    Copyright IR-BAS

    Цитирания (Citation/s):
    1. E. Larsson, “Effektivare informationsflöden med Lean information management”, DiVA (Digitala Vetenskapliga Arkivet), open access, 2022., p. 54, URN: urn:nbn:se:miun:diva-45219, id: diva2:1671302 - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    2. Gökmen TakFatih Öztürk, “BİR VANA ÜRETİM SÜRECİNDEKİ İŞ SAĞLIĞI VE GÜVENLİĞİ RİSKLERİNİN BULANIK HATA TÜRÜ VE ETKİLERİ ANALİZİ İLE DEĞERLENDİRİLMESİ”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24(47):229-248 , June 2025 , DOI: 10.55071/ticaretfbd.1624713 - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в национален форум с межд. уч.