Article Sidebar

Download
PDF
Statistic
Read Counter : 92 Download : 19

Main Article Content

Abstract

Reliability assessments are useful for determining how well products, systems, and services maintain their quality over the course of time and through various conditions. In this paper, a reliability assessment of a metalworking plant was conducted, as well as accelerated life testing of the plant's spot-welded and riveted products. The overall layout of the plant was complex, requiring the use of equations to calculate the reliability of stations being connected in series and parallel in order to determine the overall reliability of the system. Furthermore, equations for mean life and failure rate were used in determining the estimate of mean life for the tested components, as well as the rate at which the components fail, respectively. The results indicated that the metalworking plant had a reliability of 0.81 or 81%. Moreover, the results indicated that the failure data of the spot-welded products follow the exponential model, with the failure rate of the products being constant throughout the period under investigation. The failure data of the riveted products follow the Weibull model, increasing throughout the period under investigation. This study presents a procedure for aiding production and maintenance managers in conducting reliability assessments of their production systems.

Keywords

Reliability assessment Life testing Metalworking Exponential model Weibull model

Article Details

How to Cite
Wofuru-Nyenke, O. (2024). Reliability assessment and accelerated life testing in a metalworking plant. Future Technology, 3(3), 1–7. Retrieved from https://fupubco.com/futech/article/view/153
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. B. Gouveia, J. Rodrigues, and P. Martins, Ductile fracture in metalworking: experimental and theoretical research. Journal of materials processing technology, 2000. 101(1-3): pp. 52-63.
  2. N.V. Martyushev, B.V. Malozyomov, S.N. Sorokova, E.A. Efremenkov, D.V. Valuev, and M. Qi, Review models and methods for determining and predicting the reliability of technical systems and transport. Mathematics, 2023. 11(15): pp. 3317.
  3. W. Zhang, X. Gu, L. Hong, L. Han, and L. Wang, Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges. Applied Soft Computing, 2023: pp. 110066.
  4. L. Xing, Reliability in Internet of Things: Current status and future perspectives. IEEE Internet of Things Journal, 2020. 7(8): pp. 6704-6721.
  5. S. Zinchenko, A. Ben, P. Nosov, I. Popovych, P. Mamenko, and V. Mateichuk, Improving the accuracy and reliability of automatic vessel moution control system. Radio Electronics, Computer Science, Control, 2020(2): pp. 183-195.
  6. F.V. Haase and R. Woll, Assessment of reliability implementation in manufacturing enterprises. Management and Production Engineering Review, 2016.
  7. P. Yue, J. An, J. Zhang, J. Ye, G. Pan, S. Wang, P. Xiao, and L. Hanzo, Low earth orbit satellite security and reliability: Issues, solutions, and the road ahead. IEEE Communications Surveys & Tutorials, 2023.
  8. Y. Xu, S. Kohtz, J. Boakye, P. Gardoni, and P. Wang, Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges. Reliability Engineering & System Safety, 2023. 230: pp. 108900.
  9. T. Karaulova, M. Kostina, and E. Shevtshenko, Reliability assessment of manufacturing processes. International journal of industrial engineering and management, 2012. 3(3): pp. 143.
  10. M. Kostina, T. Karaulova, J. Sahno, and M. Maleki, Reliability estimation for manufacturing processes. Journal of Achievements in Materials and Manufacturing Engineering, 2012. 51(1): pp. 7-13.
  11. W. Betz, I. Papaioannou, and D. Straub, Bayesian post-processing of Monte Carlo simulation in reliability analysis. Reliability Engineering & System Safety, 2022. 227: pp. 108731.
  12. S. Lazarova-Molnar and N. Mohamed, Reliability assessment in the context of industry 4.0: data as a game changer. Procedia Computer Science, 2019. 151: pp. 691-698.
  13. O.K. Wofuru-Nyenke, T.A. Briggs, and D.O. Aikhuele, Advancements in sustainable manufacturing supply chain modelling: a review. Process Integration and Optimization for Sustainability, 2023. 7(1-2): pp. 3-27.
  14. Z. Xu and J.H. Saleh, Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering & System Safety, 2021. 211: pp. 107530.
  15. M. Aslam, M. Azam, and F. Smarandache, A new sudden death testing using repetitive sampling under a neutrosophic statistical interval system, in Optimization Theory Based on Neutrosophic and Plithogenic Sets. 2020, Elsevier. p. 137-150.
  16. K.U. Ugoji, O.E. Isaac, B. Nkoi, and O. Wofuru-Nyenke, Improving the Operational Output of Marine Vessel Main Engine System through Cost Reduction using Reliability. International Journal of Engineering and Modern Technology (IJEMT), 2022. 8(2): pp. 36-52.
  17. F. Blaabjerg, H. Wang, I. Vernica, B. Liu, and P. Davari, Reliability of power electronic systems for EV/HEV applications. Proceedings of the IEEE, 2020. 109(6): pp. 1060-1076.
  18. L. Wang, A. Kolios, X. Liu, D. Venetsanos, and R. Cai, Reliability of offshore wind turbine support structures: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 2022. 161: pp. 112250.
  19. O.K. Wofuru-Nyenke, Mechanized cover crop farming: Modern methods, equipment and technologies. Circular Agricultural Systems, 2023. 3(1).
  20. O. Wofuru-Nyenke and T. Briggs, Predicting demand in a bottled water supply chain using classical time series forecasting models. Journal of Future Sustainability, 2022. 2(2): pp. 65-80.
  21. C.E. Ebeling, An introduction to reliability and maintainability engineering. 2019: Waveland Press. ISBN: 1577666259.
  22. J.F. Lawless, Statistical models and methods for lifetime data. 2011: John Wiley & Sons. ISBN:9780471085447.
  23. R.A. Johnson, I. Miller, and J.E. Freund, Probability and statistics for engineers. 2000. ISBN10: 0134995384.