Surrogate-based Pareto optimization of annealing parameters for severely deformed steel. (15th February 2016)

• Record Type: Journal Article
• Authors: Ghiabakloo, H.; Lee, K.; Kazeminezhad, M.; Kang, B.S.
• Is Part Of: Materials & design. Volume 92(2016)
• Page Start: 1062
• Page End: 1069
• Publication Date: 2016-02-15
• Subjects: Severe plastic deformation (SPD) -- Annealing -- Regression Kriging -- Pareto optimization -- Technique for order preferences by similarity to ideal solution (TOPSIS); Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2015.11.059
• Languages: English
• ISSNs: 0264-1275

Abstract

Abstract: Severe plastic deformation (SPD) is a metalworking technique that is used for the enhancement of the strength and hardness of metallic materials. As SPD causes ductility deterioration, materials typically necessitate annealing for ductility increase; however, annealing may conversely affect strength and hardness. Thus, to optimally balance strength, hardness, and ductility, this study determined annealing conditions with a severely deformed low carbon steel sheet by adjusting annealing time and temperature. For the facilitation of the annealing process optimization, measurements of strength, hardness, and ductility under various annealing conditions were represented by regression Kriging. Then, because of the conflicting nature of the desired metal properties, a set of optimal annealing conditions was identified by Pareto multi-objective optimization. Finally, the best combination on the Pareto front was selected with TOPSIS. The results of Pareto optimization with regression Kriging showed that the best candidates for annealing conditions can be determined at a significantly reduced experimental cost. Graphical abstract: Highlights: Designing the annealing process to achieve the best combination of properties is an expensive procedure. Kriging regression method is utilized to model the annealing process. The optimal annealing conditions are found by Pareto optimization. The best annealing temperature and time are computed by TOPSIS method, based on the requirements of the subsequent applications.