Abstract
All of processes that are being performed are connected with the risk. Thus, manufacturing companies need to evaluate and react to these risks, as well as it is possible. One of the method recommended for risk assessment in production companies is Failure Mode and Effects Analysis (FMEA), which allows to calculate the risk and prioritize it. However, the FMEA is expert-knowledge based method, which makes it susceptible for the human-factor mistakes. The solution that allow to avoid uncertainty of FMEA is using the fuzzy sets, which is called fuzzy FMEA (fFMEA). The discussed case study is about the company that produces components being used in delivery vans – the production of these components need to end by the overall Final Quality Control (FQC), which means that 100% of components need to be controlled. This FQC process, like every else, is connected with the risk of mistakes. In the paper, the example of performing fuzzy FMEA in industry was described. In involves the analysis of FQC, which is very important, especially in automotive industry, where some of the possible risks or defects can result in danger for humans health or even a life. The aim of the research was to perform the risk evaluation of Final Quality Control (FQC) process, basing on the experts knowledge. The aim was reached by implementing the fuzzy FMEA method.
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References
Mrugalska B., Tytyk E.: Quality control methods for product reliability and safety. In: 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015. Procedia Manuf. 3, 2730–2737 (2015)
Myers, A.: Complex System Reliability: Multichannel Systems with Imperfect Fault Coverage. Springer-Verlag, London (2010)
Górny, A.: Minimum safety requirements for the use of work equipment (for example of control devices). In: Occupational Safety and Hygiene – Sho 2013, pp. 227–229 (2013)
Nakagawa, T.: Advanced Reliability Models and Maintenance Policies. Springer-Verlag, London (2008)
Xu, K., Tang, L.C., Xie, M., Ho, S.L., Zhu, M.L.: Fuzzy assessment of FMEA for engine systems. Reliab. Eng. Syst. Saf. 75, 17–29 (2002)
Stylidis, K., Wickman, C., Söderberg, R.: Defining perceived quality in the automotive industry: an engineering approach. In: CIRP 25th Design Conference Innovative Product Creation. Procedia CIRP, vol. 36, pp. 165–170 (2015)
Schmitt, R., Quattelbaum, B., Falk, B.: Distribution of customer perception information within the supply chain. Oper. Supply Chain Manage. 3(2), 94–104 (2010)
Burduk, A., Kochańska, J., Górnicka, D.: Calculation of labour input in multivariant production with use of simulation. In: Information Systems Architecture and Technology Proceedings. Advances in Intelligent Systems and Computing, vol. 1051, pp. 31–40 (2020)
Reis, D., Vanxo, F., Reis, J., Duarte, M.: Discriminant analysis and optimization applied to vibration signals for the quality control of rotary compressors in the production line. Arch. Acoust. 44(1), 79–87 (2019)
Nahmias, S., Olsen, T.L.: Production and Operations Analysis: Strategy, Quality, Analytics. Application. Waveland Press, Long Grove (2015)
ISO/IEC 31010:2009 Risk management—Risk assessment techniques. The International Organization for Standardization and The International Electrotechnical Commission (2009)
Sharma, R., Kumar, D., Kumar, P.: Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. Int. J. Qual. Reliab. Manage. 22, 986–1004 (2005)
Dagsuyu, C., Gocmen, E., Narli, M., Kokangul, A.: Classical and fuzzy FMEA risk analysis in a sterilization unit. Comput. Ind. Eng. 111, 286–294 (2016)
Petrovic, D.V., Tanasijevic, M., Milic, V., Lilic, N., Stojadinovic, S., Svrkota, I.: Risk assessment model of mining equipment failure based on fuzzy logic. Expert Syst. Appl. 41, 8157–8164 (2014)
Nguyen, H.: Fuzzy methods in risk estimation of the ship system failures based on the expert judgements. J. KONBiN 43, 393–403 (2017)
Tay, K.M., Lim, C.P.: Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int. J. Qual. Reliab. Manage. 23(8), 1047–1066 (2006)
Almannai, B., Greenough, R., Kay, J.: A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies. Robot. Comput. Integr. Manuf. 24, 501–507 (2008)
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Łapczyńska, D., Burduk, A. (2021). Fuzzy FMEA Application to Risk Assessment of Quality Control Process. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_30
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DOI: https://doi.org/10.1007/978-3-030-57802-2_30
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