Abstract
Multi-objective optimization is employed using genetic algorithm, for designing novel age-hardenable aluminium alloy with improved properties. Data on the mechanical properties of age-hardenable aluminium alloys is considered together for modeling the mechanical properties using artificial neural network. The models are used as objective functions to get the optimized combination of input parameters for the objectives, viz. high strength and ductility. The significance analyses of the variables on the ANN models gave a primary insight on the role of the variables. The Pareto solutions emerged from the GA based multi-objective optimization is found suitable for effective design of aluminium alloys with tailored properties. An in depth study of the role of the variables in the non-dominated solutions clearly describes the guideline for developing an alloy with improved mechanical properties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Polmear, I.J.: Light Alloys-from Traditional Alloys to Nanocrystals. Elsevier, Burlington (2006)
Raghavan, V.: Physical Metallurgy, Principles and Practice. Prentice-Hall of India Private Limited, New Delhi (2004)
Sheikh, H., Serajzadeh, S.: Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect. J. Mater. Process. Technol. 196, 115–119 (2008)
Datta, S., Chattopadhyay, P.P.: Soft computing techniques in advancement of structural metals. Int. Mater. Rev. 58, 475–504 (2013)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Pearson-Education, New Delhi (2002)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Wiley, New York (2000)
Chakraborti, N.: Genetic algorithms in materials design and processing. Int. Mater. Rev. 49, 246–260 (2004)
Das, P., Mukherjee, S., Ganguly, S., Bhattacharyay, B.K., Datta, S.: Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of NN and desirability function. Comput. Mater. Sci. 45, 104–110 (2009)
Ganguly, S., Datta, S., Chakraborti, N.: Genetic algorithm based search on the role of variables in the work hardening process of multiphase steels. Comput. Mater. Sci. 45, 158–166 (2009)
Mohanty, I., Bhattacharjee, D., Datta, S.: Designing cold rolled IF steel sheets with optimized tensile properties using ANN and GA. Comput. Mater. Sci. 50, 2331–2337 (2011)
Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)
Haykin, S.: Neural Networks: A Comprehensive Foundation. McMillan, New York (1994)
Nigrin, A.: Neural Networks for Pattern Recognition. The MIT Press, Cambridge (1993)
Song, R.G., Zhang, Q.Z.: Heat treatment technique optimization for 7175 aluminium alloy by artificial neural network and genetic algorithm. J. Mater. Process. Technol. 117, 84–88 (2001)
Song, R.G., Zhang, Q.Z., Tseng, M.K., Zhang, B.J.: The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminium alloys. Mater. Sci. Eng. C 3, 39–41 (1995)
Durmus, H.K., Ozkaya, E., Meric, C.: The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminium alloy. Mater. Des. 27, 156–159 (2006)
Kundu, M., Ganguly, S., Datta, S., Chattopadhyay, P.P.: Simulating time temperature transformation diagram of steel using artificial neural network. Mater. Manuf. Processes 24, 169–173 (2009)
Mohanty, I., Datta, S., Bhattacharjee, D.: Composition-processing-property correlation of cold rolled IF steel sheets using neural network. Mater. Manuf. Processes 24, 100–105 (2009)
Datta, S., Sil, J., Banerjee, M.K.: Petri neural network model for the effect of controlled thermomechanical process parameters on the mechanical properties of HSLA steel. ISIJ Int. 39, 786–791 (1999)
ASM Handbook: Volume 2: Properties and Selection: Nonferrous Alloys and Special‐Purpose Materials. ASM International Handbook Committee (1990)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Olden, J.D., Joy, M.K., Russell, G.: An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model. 178, 389–397 (2004)
Acknowledgement
Swati Dey acknowledges Council of Scientific and Industrial Research, India for financial support to carry out this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dey, S., Ganguly, S., Datta, S. (2015). In silico Design of High Strength Aluminium Alloy Using Multi-objective GA. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-20294-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20293-8
Online ISBN: 978-3-319-20294-5
eBook Packages: Computer ScienceComputer Science (R0)