Computer Aided Design Solution Based on Genetic Algorithms for FMEA and Control Plan in Automotive Industry
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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:70235", author = "Nadia Belu and Laurentiu M. Ionescu and Agnieszka Misztal", title = "Computer Aided Design Solution Based on Genetic Algorithms for FMEA and Control Plan in Automotive Industry", abstract = "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, control plan, FMEA.", volume = "9", number = "8", pages = "2672-6", }