Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31242
Computer Aided Design Solution Based on Genetic Algorithms for FMEA and Control Plan in Automotive Industry

Authors: Nadia Belu, Laurentiu M. Ionescu, Agnieszka Misztal


In this paper we propose a computer-aided solution with Genetic Algorithms in order to reduce the drafting of reports: FMEA analysis and Control Plan required in the manufacture of the product launch and improved knowledge development teams for future projects. The solution allows to the design team to introduce data entry required to FMEA. The actual analysis is performed using Genetic Algorithms to find optimum between RPN risk factor and cost of production. A feature of Genetic Algorithms is that they are used as a means of finding solutions for multi criteria optimization problems. In our case, along with three specific FMEA risk factors is considered and reduce production cost. Analysis tool will generate final reports for all FMEA processes. The data obtained in FMEA reports are automatically integrated with other entered parameters in Control Plan. Implementation of the solution is in the form of an application running in an intranet on two servers: one containing analysis and plan generation engine and the other containing the database where the initial parameters and results are stored. The results can then be used as starting solutions in the synthesis of other projects. The solution was applied to welding processes, laser cutting and bending to manufacture chassis for buses. Advantages of the solution are efficient elaboration of documents in the current project by automatically generating reports FMEA and Control Plan using multiple criteria optimization of production and build a solid knowledge base for future projects. The solution which we propose is a cheap alternative to other solutions on the market using Open Source tools in implementation.

Keywords: Automotive industry, FMEA, control plan

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2498


[1] McDermott R., Mikulak R., Beauregard M., The basics of FMEA, 2nd Edition, Taylor & Francis Group, 270 Madison Avenue, New York, 2009.
[2] S. Helvacioglu and E. Ozen, Fuzzy based failure modes and effect analysis for yacht system design, Ocean Engineering, vol.79, pp. 131– 141, March, 2014.
[3] Chrysler Corporation, Ford Motor Company, General Motors Corporation, Potential Failure Modes and Effects Analysis (FMEA). Reference Manual, 4th ed., 2008.
[4] ISO/TS 16949:2009, Quality management systems. Particular requirements for the application of ISO 9001:2008 for automotive production and relevant service part organizations, International Organization for Standardization, Geneva, Switzerland 2009.
[5] Advanced Product Quality Planning and Control Plan APQP. Reference Manual. 2nd Edition. AIAG, 2008.
[6] N. Belu, A.-R. Al Ali and N. Khassawneh, Application of Control Plan - PPAP Tool in Automotive Industry Production, Quality - Access to Success, vol. 14, no. 136 pp. 68-72, October, 2013.
[7] A. Maria Jaya Prakasha, T. Senthilvelan and R. Gnanadass “Optimization of process parameters through fuzzy logic and genetic algorithm – A case study in a process industry”, Applied Soft Computing, vol. 30, pp. 94–103, May 2015.
[8] Z. Yang, S. Bonsall and J. Wang, Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA, IEEE Transactions on Reliability, vol. 57, pp. 517–528, 2008.
[9] J. Yang, H.-Z. Huang, L.-P. He, S.-P. Zhu, D. Wen, Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty, Engineering Failure Analysis, vol. 18 pp. 2084–2092, 2011.
[10] H.-C. Liu, L. Liu, Q.-H. Bian, Q.-L. Lin, N. Dong, P.-C. Xu, Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory, Expert Systems Applications, vol. 38, pp. 4403–4415, 2011.
[11] Y.-M. Wang, K.-S. Chin, G.K.K. Poon and J.-B.Yang, Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean, Expert Systems Applications, vol. 36, pp. 1195–1207, 2009.
[12] C. Ştirbu, C. Anton, L. Stirbu and R Badea, Improved by prediction of the PFMEA using the artificial neural networks in the electrical industry, International Conference on Applied Electronics, Pilsen, September 2011.
[13] C. L. Chang, P. H Liu and C. C Wei, Failure mode and effects analysis using grey theory, Integrated Manufacturing Systems, vol. 12(3), pp.211–216, 2001.
[14] K. S., Chin, Y. M. Wang, G. K. K. Poon, and J. B. Yang, Failure mode and effects analysis by data envelopment analysis, Decision Support Systems, vol. 48(1), pp. 246–256, 2009.
[15] K. H. Chang and C. H. Cheng, Evaluating the risk of failure using the fuzzy OWA and DEMATEL method, Journal of Intelligent Manufacturing, vol. 22(2), pp. 113–129, 2011.
[16] J. Holland, Adaptation in Natural and Artificial Systems, Cambridge, MA: MIT Press, 1992.
[17] A. Misztal, N. Belu, N. Rachieru, “Comparative analysis of awareness and knowledge of APQP requirements in Polish and Romanian automotive industry”, Applied Mechanics and Materials, Vol. 657 (2014) pp. 981-985.
[18] M. Butlewski, M. Jasiulewicz-Kaczmarek, A. Misztal, M. Sławińska, “Design methods of reducing human error in practice”, in: Safety and Reliability: Methodology and Applications - Proceedings of the European Safety and Reliability Conference ESREL 2014 Wrocław, (ed.) T. Nowakowski, M. Młyńczak, A. Jodejko-Pietruczuk, S. Werbińska-Wojciechowska, pp. 1101-1106, CRC Press, London 2015.