Autors: Tsenev, V. P., Ivanova, M. S.
Title: Statistical and machine learning approach for evaluation of control systems for automatic production lines
Keywords: R&R statistical method, Measurement system analysis, Statistical process control, Supervised machine learning, Automatic production line

Abstract: The manufacturing processes and the control systems for automatic production lines mainly are evaluated through usage of statistical methods as recently machine learning algorithms are also used. The aim of the paper is to present an approach for control measurement systems evaluation, based on a combination of statistical techniques like attribute Repeatability& Reproducibility analysis, measurement system analysis and supervised machine learning algorithms like Random Forest and k-NN. The proposed method is verified in the production of the G8680x connector, which is used in the automotive industry. The control is performed 100% for all manufactured parts immediately after the “injection molding” process. It is proved that taking advantages of the statistics and machine learning, the manufacturing process and control measurement systems could be evaluated with very high accuracy. The exploration and analysis leads to the formulation of some recommendations in support of process engi

References

    Issue

    Bulletin of Electrical Engineering and Informatics, vol. 11, issue 5, pp. 2527-2536, 2022, Indonesia, DOI 10.11591/eei.v11i5.3664

    Цитирания (Citation/s):
    1. Ahmed Hussein Ali, Mostafa Abduhgafoor Mohammed, Raed Abdulkareem Hasan, Maan Nawaf Abbod, Mohammed Sh. Ahmed, Tole Sutikno, “Big data classification based on improved parallel k-nearest neighbor “, TELKOMNIKA Telecommunication, Computing, Electronics and Control, Vol 21, No 1, 2023, Indonesia, ISSN: 1693-6930, e-ISSN: 2302-9293, DOI:http://doi.org/10.12928/telkomnika.v21i1.24290, Q3, SJR 0.314 - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus