Acta Marisiensis.
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Year 2024
Volume 21 (XXXVIII), no 1 Volume 21 (XXXVIII), no 2 Year 2023 Volume 20 (XXXVII), no 1 Volume 20 (XXXVII), no 2 Year 2022 Volume 19 (XXXVI), no 1 Volume 19 (XXXVI), no 2 Year 2021 Volume 18 (XXXV), no 1 Volume 18 (XXXV), no 2 Year 2020 Volume 17 (XXXIV), no 1 Volume 17 (XXXIV), no 2 Year 2019 Volume 16 (XXXIII), no 1 Volume 16 (XXXIII), no 2 Year 2018 Volume 15 (XXXII), no 1 Volume 15 (XXXII), no 2 Year 2017 Volume 14 (XXXI), no 1 Volume 14 (XXXI), no 2 Year 2016 Volume 13 (XXX), no 1 Volume 13 (XXX), no 2 Year 2015 Volume 12 (XXIX), no 1 Volume 12 (XXIX), no 2 Year 2014 Volume 11 (XXVIII), no 1 Volume 11 (XXVIII), no 2 Year 2013 Volume 10 (XXVII), no 1 Volume 10 (XXVII), no 2 Year 2012 Volume 9 (XXVI), no 1 Volume 9 (XXVI), no 2 Year 2011 Volume 8 (XXV), no 1 Volume 8 (XXV), no 2 Year 2010 Volume 7 (XXIV), no 1 Volume 7 (XXIV), no 2 Year 2009 Volume 6 (XXIII) |
2024, Volume 21 (XXXVIII), no 2
Alex-Barna KACSÓ, University of Medicine, Pharmacy, Science and Technology ”G.E. Palade” of Târgu Mureș, Romania Abstract: This paper presents the results of the design and optimization of a manufacturing line for automotive products. The study focuses on optimizing a station to ensure that a shutdown of one manufacturing line does not affect other lines. The optimization process utilized KUKA robots for parts handling and implemented a rack-type station where automotive parts can be placed or picked up by two robots simultaneously. The station was designed to use modular design elements. The rack station is equipped with NOK define before abbreviate part identification sensors, resulting in a significant increase in production efficiency (28%), improved product quality (identification of scrap parts before welding the final product), and reduced operational costs. Future research may explore the long-term impact of optimization and its effects on employees. DOI: https://doi.org/10.62838/amset-2024-0014 Pages: 26-30 View full article |
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Update: 18-Dec-2024 | © Published by University Press |