Multiobjective optimal design of friction stir welding considering quality and cost issues

References

• Chakraborti N.: ‘Critical assessment 3: the unique contributions of multi-objective evolutionary and genetic algorithms in materials research’, Mater. Sci. Technol., 2014, 30, (11), 1259–1262.
   Web of Science ®Google Scholar
• Paszkowicz W.: ‘Genetic algorithms, a nature-inspired tool: a survey of applications in materials science and related fields: part II’, Mater. Manuf. Processes, 2013, 28, (7), 708–725.
   Web of Science ®Google Scholar
• Datta S., Zhang Q., Sultana N. and Mahfouf M.: ‘Optimal design of titanium alloys for prosthetic applications using a multi-objective based genetic algorithm’, Mater. Manuf. Processes, 2013, 28, (7), 741–745.
   Web of Science ®Google Scholar
• Zhang Q., Mahfouf M., Yates J. R., Pinna C., Panoutsos G., Boumaiza S., Greene R. J. and de Leon L.: ‘Modeling and optimal design of machining-induced residual stresses in aluminium alloys using a fast hierarchical multiobjective optimization algorithm’, Mater. Manuf. Processes, 2011, 26, (3), 508–520.
   Web of Science ®Google Scholar
• Zhang Q. and Mahfouf M.: ‘A modified PSO with a dynamically varying population and its application to the multi-objective optimal design of alloy steels’, Proc. 2009 IEEE Cong. on ‘Evolutionary Computation’, Trondheim, Norway, 2009, IEEE 3241–3248.
   Google Scholar
• Tutum C. C. and Hattel J. H.: ‘Numerical optimisation of friction stir welding: review of future challenges’, Sci. Technol. Weld. Join., 2011, 16, (4), 318–324.
   Web of Science ®Google Scholar
• Tansel I. N., Demetgul M., Okuyucu H. and Yapici A.: ‘Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network’, Int. J. Adv. Manuf. Technol., 2010, 48, 95–101.
   Web of Science ®Google Scholar
• Roshan S. B., Jooibari M. B., Teimouri R., Asgharzadeh-Ahmadi G., Falahati-Naghibi M. and Sohrabpoor H.: ‘Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm’, Int. J. Adv. Manuf. Technol., 2013, 69, 1803–1818.
   Web of Science ®Google Scholar
• Parida B. and Pal S.: ‘Fuzzy assisted grey Taguchi approach for optimisation of multiple weld quality properties in friction stir welding process’, Sci. Technol. Weld. Join., 2015, 20, (1), 35–41.
   Web of Science ®Google Scholar
• Tutum C. C. and Hattel J. H.: ‘Optimization of process parameters in friction stir welding based on residual stress analysis: a feasibility study’, Sci. Technol. Weld. Join., 2010, 15, (5), 369–377.
   Web of Science ®Google Scholar
• Tutum C. C., Deb K. and Hattel J. H.: ‘Multi-criteria optimization in friction stir welding using a thermal model with prescribed material flow’, Mater. Manuf. Processes, 2013, 28, (7), 816–822.
   Web of Science ®Google Scholar
• Shojaeefard M. H., Behnagh R. A., Akbari M., Givi M. K. B. and Farhani F.: ‘Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm’, Mater. Des., 2013, 44, 190–198.
   Web of Science ®Google Scholar
• El-Danaf E. A., El-Rayes M. M. and Soliman M. S.: ‘Friction stir processing: an effective technique to refine grain structure and enhance ductility’, Mater. Des., 2010, 31, 1231–1236.
   Web of Science ®Google Scholar
• Thomas W. M. and Gittos M. F.: ‘Development of friction stir tools for the welding of thick (25mm) aluminium alloys’, TWI Members Report 694/1999, TWI, UK, 1999.
   Google Scholar
• Beamish K. A. and Russell M. J.: ‘Relationship between the features on an FSW tool and weld microstructure’, Proc. 8th Int. Symp. on ‘Friction Stir Welding’, Timmendorfer Strand, Germany; 2010.
   Google Scholar
• Mishra R. S. and Ma Z. Y.: ‘Friction stir welding and processing’, Mater. Sci. Eng. R Rep., 2005, 50, (1–2), 1–78.
   Web of Science ®Google Scholar
• Nandan R., DebRoy T. and Bhadeshia H. K. D. H.: ‘Recent advances in friction stir welding—process, weldment structure and properties’, Prog. Mater. Sci., 2008, 53, 980–1023.
   Web of Science ®Google Scholar
• Li B., Shen Y. and Hu W.: ‘The study on defects in aluminum 2219-T6 thick butt friction stir welds with the application of multiple non-destructive testing methods’, Mater. Des., 2011, 32, 2073–2084.
   Web of Science ®Google Scholar
• Dieter G. E.: ‘Mechanical metallurgy: SI metric edition’; 1988, London, McGraw-Hill.
   Google Scholar
• Zhang Q., Mahfouf M., Panoutsos G., Beamish K. and Norris I.: ‘Knowledge discovery for friction stir welding via data driven approaches: part 1—correlation analyses of internal process variables and weld quality’, Sci. Technol. Weld. Join., 2012, 17, (8), 672–680.
   Web of Science ®Google Scholar
• Zhang Q., Mahfouf M., Panoutsos G., Beamish K. and Norris I.: ‘Knowledge discovery for friction stir welding via data driven approaches part 2 – multiobjective modelling using fuzzy rule based systems’, Sci. Technol. Weld. Join., 2012, 17, (8), 681–693.
   Web of Science ®Google Scholar
• Zhang Q. and Mahfouf M.: ‘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’, Appl. Soft Comput., 2011, 11, (2), 2419–2443.
   Web of Science ®Google Scholar
• Zhang Q., Mahfouf M., Yates J. R. and Pinna C.: ‘Model fusion using fuzzy aggregation: special applications to metal properties’, Appl. Soft Comput., 2012, 12, (6), 1678–1692.
   Web of Science ®Google Scholar
• Zhang Q., Mahfouf M., Panoutsos G., Beamish K. and Norris I.: ‘Systems modelling of the internal process variables for friction stir welding using genetic multi-objective fuzzy rule-based systems’, Proc. 9th Int. Conf. on ‘Trends in Welding Research’, Chicago, IL, USA, 2012, ASM International 834–841.
   Google Scholar
• Zhang Q. and Mahfouf M.: ‘A new reduced space searching algorithm (RSSA) and its application in optimal design of alloy steels’, Proc. 2007 IEEE Cong. on ‘Evolutionary Computation’, Singapore, 2007, IEEE 1815–1822.
   Google Scholar
• Zhang Q. and Mahfouf M.: ‘A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels’, Eng. Appl. Artif. Intell., 2010, 23, (5), 660–675.
   Web of Science ®Google Scholar
• Jin Y., Olhofer M. and Sendhoff B.: ‘Dynamic weighted aggregation for evolutionary multi-objective optimization: why does it work and how?’, Proc. Genetic and Evolutionary Computation Conf., San Francisco, CA, USA, 2001, ACM 1042–1049.
   Google Scholar
• Deb K., Sundar J., Udaya N. and Chaudhuri S.: ‘Reference point based multi-objective optimization using evolutionary algorithms’, Int. J. Comput. Intell. Res., 2006, 2, (6), 273–286.
   Google Scholar
• Branke J., Deb K., Dierolf H. and Osswald M.: ‘Finding knees in multi-objective optimization’, in ‘PPSN VIII, LNCS 3242’, (ed. Yao X et al.., 722–731; 2004, Heidelberg, Springer.
   Google Scholar
• Humphreys F. J. and Hotherly M.: ‘Recrystallization and related annealing phenomena’; 1995, New York, Pergamon Press.
   Google Scholar