Estimation of Workload for Aircraft Design Projects By Using Fuzzy Logic

Yıl 2019, Cilt: 17 Sayı: 1, 21 - 30, 30.12.2019

Veysel Cenk Karakuz Özlem Müge Testik

Öz

In this study, it is aimed to estimate workload for aircraft design.
Actual values of variables are not always reachable due to subjectivity, lack
of data, and so on. In this case, forming the model by incorporating fuzzy
logic can be a satisfactory solution. In this study, the results obtained by
fuzzified aircraft design data from literature were compared with the actual
results of the literature. According to comparison results, it is seen that the
results of the fuzzy estimation method are close to the actual results. As a
conclusion, estimation is practicable by fuzzy variables and satisfactory
results can be achieved when inputs are uncertain in aircraft design.

Anahtar Kelimeler

Aircraft, Design, Workload, Cost estimation, fuzzy logic, fuzzy estimation

Destekleyen Kurum

Türk Havacılık ve Uzay Sanayii A.Ş.

Kaynakça

• 1. Niazi, A., Dai, J. S., Balabani, S. and Seneviratne, L., Product Cost Estimation: Technique Classification and Methodology Review, Journal of Manufacturing Science and Engineering, 128 (2006) 2, 563-575.
• 2. Zadeh, L. A., Fuzzy sets, Information and Control, 8 (1965) 3, 338-353.
• 3. Kiernan, K. M., The Wright Brothers vs. the World: Understanding the Wright Patent Wars, Journal of Aviation/Aerospace Education & Research, 21 (2012) 3, 41-44.
• 4. Atkinson, P., The Design Journal and the Meaning of Design, The Design Journal, vol. 20, no. 1, pp. 1-4, 2017.
• 5. Villecco, F. and Pellegrino, A., Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems, Entropy, 19 (2017) 475, 1-8.
• 6. Daalhuizen, J., Badke-Schaub, P. and Batill, S., Dealing with uncertainty in design practice: issues for designer-centered methodology, International Conference on Engineering Design, ICED'09, 24-27 Agust, California, 147-158, 2009.
• 7. MacCormack, A., Verganti, R. and Iansiti, M., Developing Products on Internet Time: The Anatomy of a Flexible Development Process, Management Science, 47 (2001) 1, 133-150.
• 8. Bstieler, L., The Moderating Effect of Environmental Uncertainty on New Product Development and Time Efficiency, Product Innovation Management, 22 (2005), 267-284.
• 9. Aram, S., Eastman, C. and Beetz, J., Qualitative and Quantitative Cost Estimation: A Methodology Analysis, Computing in Civil and Building Engineering, Joint CIB W78 and ICCCBE Conference, Orlando, 2014.
• 10. Fielding, J. P., Introduction to Aircraft Design, Cambridge University Press, Cambridge, 1999.
• 11. Gudmundsson, S., General Aviation Aircraft Design: Applied Methods And Procedures, Oxford: Butterworth-Heinemann, 2014.
• 12. Sadraey, M. H., Aircraft Design: A Systems Engineering Approach, Wiley, Chichester, 2013.
• 13. Raymer , D. P., Aircraft Design: A Conceptual Approach, Princeton University Press, Washington, 1989.
• 14. Burkimsher, A., Bate, I. and Indrusiak, L. S., A Characterisation Of The Workload On An Engineering Design Grid, In Proceedings of the High Performance Computing Symposium (HPC ’14), Tampa, FL, USA, 2014.
• 15. Lai, Y.-J. and Hwang, C.-L., Fuzzy Mathematical Programming, Berlin Heidelberg: Springer- Verlag, 1992.
• 16. Dobrescu, E. and Balazinski, M., Fuzzy logic aircraft environment controller, IEEE Annual Meeting of the Fuzzy Information, Processing NAFIPS '04, Banff, Alberta, Canada, 2004.
• 17. Atlı, Ö. and Kahraman, C., Aircraft Maintenance Planning Using Fuzzy Critical Path Analysis, International Journal of Computational Intelligence Systems, 5 (2012) 3, 553-567.
• 18. Lo, C. H., Fung, E. H. K. and Wong, Y. K., Intelligent Automatic Fault Detection for Actuator Failures in Aircraft, IEEE Transactions on Industrial informatics, 5 (2009) 1, 50-55.
• 19. Liu, Y., Yao, K. and Lu, Y., Research on fuzzy logic control methods for Mars airplane, Aircraft Engineering and Aerospace Technology: An International Journal, 86 (2014) 5, 415-422.
• 20. Ross, T. J., Fuzzy Logic With Engineering Applications, John Wiley & Sons Ltd, Chichester, 2004.
• 21. Klir, G. J. and Yuan, B., Fuzzy Sets and Fuzzy Logic, USA: Prentice Hall, 1995.
• 22. Ziauddin, Kamal, S., Khan, S. and Nasir, J. A., A Fuzzy Logic Based Software Cost Estimation Model International Journal of Software Engineering and Its Applications, 7 (2013) 2, 7-16.
• 23. Novák, V. and Lehmke, S., Logical structure of fuzzy IF-THEN rules, Fuzzy Sets and Systems, 157 (2006) 15, 2003-2029.
• 24. Naaz, S., Alam, A. and Biswas, R., Effect of different defuzzification methods in a fuzzy based load balancing application, International Journal of Computer Science Issues, 8 (2011) 5, 261-267.
• 25. Karakuz, V. C., Fuzzy Forecasting Of Workload For Aircraft Design Projects, Master’s Thesis, Graduate School of Science and Engineering of Hacettepe University, 2019.