Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes
More details
Hide details
Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Poland
Department of Industrial Engineering and Informatics, Technical University of Košice, Slovak Republic
Submission date: 2024-05-14
Final revision date: 2024-06-03
Acceptance date: 2024-06-04
Online publication date: 2024-06-07
Publication date: 2024-06-10
Corresponding author
Anna Burduk   

Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Poland
The article presents the application of fuzzy logic to risk assessment in assembly and forming production processes. The fuzzy FMEA method was used, enabling the assessment of risk parameters based on expert opinions. This resulted in the development of a system that allows for greater flexibility and increased resistance to errors associated with human factors, enabling risk assessment through the use of linguistic variables. This allows organisations to analyse and manage risk, improving the efficiency and safety of their operations. This article presents an analysis of the benefits of using fuzzy logic in risk assessment in production in conjunction with the FMEA method, which is one of the most widely used risk assessment methods in industry. It discusses how fuzzy logic can help capture uncertainties in production processes and provide a more flexible framework for their evaluation. A case study is also presented, in which fuzzy logic was applied to risk assessment, highlighting the benefits it brings to production efficiency and safety.
ZADEH L.A., 1975, The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Information Sciences, 8, 199-249.
ISO/IEC 31010: 2019 Risk management – Risk assessment techniques, The International Organization for Standardization and the International Electrotechnical Commission.
ZUNIGA A. A., FERNANDES J.F.P., BRANCO P.J.C., 2023, Fuzzy-Based Failure Modes, Effects, and Criticality Analysis Applied to Cyber-Power Grids, Energies, 16/3346.
ÖZLEM M.T., EZGI T.U., 2022, Fuzzy FMEA in Risk Assessment for Test and Calibration Laboratories, Quality and Reliability Engineering International, 39/2, 575–589.
DAG S., 2020, Fuzzy Failure Mode and Effects Analysis in a Pharmaceutical Production Process with Fuzzy PROMETHEE Method, Multi-Criteria Decision Analysis in Management, IGI Global, 310–334.
JIN G., MENG Q., FENG W., 2022, Optimization of Logistics System with Fuzzy FMEA-AHP Methodology, Processes, 10/1973.
NINDA N.S., ANGGRIANI P., YUDI S., 2022, The Application of Fuzzy FMEA and TOPSIS Methods in Agricultural Supply Chain Risk Management (Case Study: Kabupaten Paser), Teknika, 18/1, 22–23.
RAHIMDEL M.J., ARYAFAR A., VAZIRI S., 2022, Fuzzy FMEA for the Safety Risk Analysis of Underground Coal Mining (A Case Study in Iran), Mining Technology, 131/2, 104–114.
LASZLO D., 2022, Fuzzy FMEA Risk Assessment Approach for IFF System in Military Helicopters Using Matlab R2022A, Katonai Logisztika, 1/2.
KADENA E., KOCAK S., TAKACS-GYORGY K., KESZTHELYI A., 2022, FMEA in Smartphones: a Fuzzy Approach, Mathematics, 10/3, 513.
YAZICI K., COKLER S.H., BORAN S., 2021, An Integrated SMED-Fuzzy FMEA Model for Reducing Setup Time, Journal of Intelligent Manufacturing, 32/6, 1547–1561.
KAZAN C.A., KORUCA H., KARATOP B., 2023, Cost Optimization with Internet Supported FMEA and Fuzzy FMEA Analysis, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 10/2, 950-970.
ŁAPCZYNSKA D., BURDUK A., 2023, Application of Fuzzy Logic to The Risk Assessment of Production Machines Failures, Lecture Notes in Networks and Systems, 749.
MathWorks Help Center, https://www.mathworks.com/.
NGUYHEN H., 2017, Fuzzy Methods in Risk Estimation of The Ship System Failures Based on the Expert Judgements, Journal of KONBiN, 43, 393-403.
RYCZYŃSKI J., TUBIS A. A., 2021, Tactical Risk Assessment Method for Resilient Fuel Supply Chains for a Military Peacekeeping Operation, Energies, 14/4679.
SADOLLAH A., 2018, Introductory Chapter: which Membership Function is Appropriate in Fuzzy System?, Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications.
Journals System - logo
Scroll to top