Optimisation in Friction Stir Welding: Modelling, Monitoring and Design (original) (raw)

References

1. Thomas, W. M., Nicholas, E. D., Needham, J. C., Murch, M. G., Templesmith, P., & Dawes, C. J. (1991). G.B. Patent Application No. 9125978.8, December 1991.
   Google Scholar
2. Nandan, R., DebRoy, T., & Bhadeshia, H. K. D. H. (2008). Recent advances in friction stir welding—process, weldment structure and properties. Progress in Materials Science, 53, 980–1023.
   Article Google Scholar
3. Chakraborti, N. (2014). Critical assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Materials Science and Technology, 30(11), 1259–1262.
   Article Google Scholar
4. Paszkowicz, W. (2013). Genetic algorithms, a nature-inspired tool: A survey of applications in materials science and related fields: Part II. Materials and Manufacturing Processes, 28(7), 708–725.
   Article Google Scholar
5. Datta, S., Zhang, Q., Sultana, N., & Mahfouf, M. (2013). Optimal design of titanium alloys for prosthetic applications using a multi-objective based genetic algorithm. Materials and Manufacturing Processes, 28(7), 741–745.
   Article Google Scholar
6. Zhang, Q., Mahfouf, M., Yates, J. R., Pinna, C., Panoutsos, G., Boumaiza, S., et al. (2011). Modeling and optimal design of machining-induced residual stresses in aluminium alloys using a fast hierarchical multiobjective optimization algorithm. Materials and Manufacturing Processes, 26(3), 508–520.
   Article Google Scholar
7. Zhang, Q., & Mahfouf, M. (2009). A modified PSO with a dynamically varying population and its application to the multi-objective optimal design of alloy steels. In Proceedings of the 2009 IEEE Cong. on ‘Evolutionary Computation’, Trondheim, Norway (pp. 3241–3248). IEEE.
   Google Scholar
8. Tansel, I. N., Demetgul, M., Okuyucu, H., & Yapici, A. (2010). Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network. International Journal of Advanced Manufacturing Technology, 48, 95–101.
   Article Google Scholar
9. Roshan, S. B., Jooibari, M. B., Teimouri, R., Asgharzadeh-Ahmadi, G., Falahati-Naghibi, M., & Sohrabpoor, H. (2013). Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm. International Journal of Advanced Manufacturing Technology, 69, 1803–1818.
   Article Google Scholar
10. Parida, B., & Pal, S. (2015). Fuzzy assisted grey Taguchi approach for optimisation of multiple weld quality properties in friction stir welding process. Science and Technology of Welding and Joining, 20(1), 35–41.
    Article Google Scholar
11. Tutum, C. C., & Hattel, J. H. (2010). Optimization of process parameters in friction stir welding based on residual stress analysis: A feasibility study. Science and Technology of Welding and Joining, 15(5), 369–377.
    Article Google Scholar
12. Tutum, C. C., Deb, K., & Hattel, J. H. (2013). Multi-criteria optimization in friction stir welding using a thermal model with prescribed material flow. Materials and Manufacturing Processes, 28(7), 816–822.
    Article Google Scholar
13. Shojaeefard, M. H., Behnagh, R. A., Akbari, M., Givi, M. K. B., & Farhani, F. (2013). Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Materials and Design, 44, 190–198.
    Article Google Scholar
14. Zhang, Q., & Mahfouf, M. (2010). A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels. Engineering Applications of Artificial Intelligence, 23(5), 660–675.
    Article Google Scholar
15. Zhang, Q., & Mahfouf, M. (2007). A new reduced space searching algorithm (RSSA) and its application in optimal design of alloy steels. In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, Singapore (pp. 1815–1822).
    Google Scholar
16. Zhang, Q., Mahfouf, M., Panoutsos, G., Beamish, K., & Norris, I. (2012a). Knowledge discovery for friction stir welding via data driven approaches: Part 1—correlation analyses of in-process variables and weld quality. Science and Technology of Welding and Joining, 17(8), 672–680.
    Article Google Scholar
17. Zhang, Q., Mahfouf, M., Panoutsos, G., Beamish, K., & Norris, I. (2012b). Knowledge discovery for friction stir welding via data driven approaches part 2—multiobjective modelling using fuzzy rule based systems. Science and Technology of Welding and Joining, 17(8), 681–693.
    Article Google Scholar
18. Zhang, Q., Mahfouf, M., Panoutsos, G., Beamish, K., & Liu, X. (2015). Multiobjective optimal design of friction stir welding considering quality and cost issues. Science and Technology of Welding and Joining, 20(7), 607–615.
    Article Google Scholar
19. El-Danaf, E. A., El-Rayes, M. M., & Soliman, M. S. (2010). Friction stir processing: An effective technique to refine grain structure and enhance ductility. Materials and Design, 31, 1231–1236.
    Article Google Scholar
20. Thomas, W. M., & Gittos, M. F. (1999). Development of friction stir tools for the welding of thick (25 mm) aluminium alloys. TWI Members Report 694/1999, TWI, UK.
    Google Scholar
21. Beamish, K. A., & Russell, M. J. (2010). Relationship between the features on an FSW tool and weld microstructure. In Proceedings of the 8th International Symposium on ‘Friction Stir Welding’, Timmendorfer Strand, Germany.
    Google Scholar
22. Mishra, R. S., & Ma, Z. Y. (2005). Friction stir welding and processing. Materials Science and Engineering: R: Reports, 50(1–2), 1–78.
    Article Google Scholar
23. Jin, Y., Olhofer, M., & Sendhoff, B. (2001). Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how? In _Proceedings of the Genetic and Evolutionary Computation Conf_erence (pp. 1042–1049). ACM: San Francisco, USA.
    Google Scholar
24. Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Multi-objective genetic algorithm and its applications to flowshop scheduling. Computers & Industrial Engineering, 30(4), 957–968.
    Article Google Scholar
25. Gaafer, A. M., Mahmoud, T. S., & Mansour, E. H. (2010). Microstructural and mechanical characteristics of AA7020-O Al plates joined by friction stir welding. Materials Science and Engineering A, 527, 7424–7429.
    Article Google Scholar
26. Boldsaikhana, E., Corwinb, E. M., Logarb, A. M., & Arbegast, W. J. (2011). The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding. Applied Soft Computing, 11, 4839–4846.
    Article Google Scholar
27. Lee, B., & Tarng, Y. S. (1999). Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. International Journal of Advanced Manufacturing Technology, 15(4), 238–243.
    Article Google Scholar
28. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, 3, 28–44.
    Article MathSciNet Google Scholar
29. Zhang, Q., & Mahfouf, M. (2008). Mamdani-type fuzzy modelling via hierarchical clustering and multi-objective particle swarm optimisation (FM-HCPSO). International Journal of Computational Intelligence Research, 4(4), 314–328.
    Article Google Scholar
30. Zhang, Q., & Mahfouf, M. (2011). A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels. Applied Soft Computing, 11(2), 2419–2443.
    Article Google Scholar
31. Hashimoto, T., Jyogan, S., Nakata, K., Kim, Y. G., & Ushio, M. (1999). FSW joints of high strength aluminum alloy. In Proceedings of the First International Symposium on Friction Stir Welding, Thousand Oaks, CA.
    Google Scholar
32. Ma, Z. Y., Mishra, R. S., & Mahoney, M. W. (2002). Superplastic deformation behaviour of friction stir processed 7075Al alloy. Acta Materialia, 50(17), 4419–4430.
    Article Google Scholar
33. Biallas, G., Braun, R., Donne, C. D., Staniek, G., & Kaysser, W. A. (1999). Mechanical properties and corrosion behavior of friction stir welds. In Proceedings of the First International Symposium on Friction Stir Welding, Thousand Oaks, CA.
    Google Scholar
34. Zadeh, L. A. (1972). A fuzzy-set-theoretic interpretation of linguistic hedges. Journal of Cybernetics, 2, 4–34.
    Article MathSciNet Google Scholar
35. Chu, T. C., Ranson, W. F., & Sutton, M. A. (1985). Applications of digital-image-correlation techniques to experimental mechanics. Experimental Mechanics, 25(3), 232–244.
    Article Google Scholar
36. Humphreys, F. J., & Hotherly, M. (1995). Recrystallization and related annealing phenomena. New York, USA: Pergamon Press.
    Google Scholar

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