Effects of coupling anisotropic yield functions with the optimization process of extruded aluminum front rail geometries in crashworthiness. (1st December 2017)

• Record Type: Journal Article
• Authors: Kohar, Christopher P.; Brahme, Abhijit; Imbert, Jose; Mishra, Raja K.; Inal, Kaan
• Is Part Of: International journal of solids and structures. Volume 128(2017)
• Page Start: 174
• Page End: 198
• Publication Date: 2017-12-01
• Subjects: Aluminum -- Anisotropy -- Crush tubes -- Crashworthiness -- Optimization; Mechanics, Applied -- Periodicals; Structural analysis (Engineering) -- Periodicals; Elastic solids -- Periodicals; Mécanique appliquée -- Périodiques; Constructions, Théorie des -- Périodiques; Solides élastiques -- Périodiques; Elastic solids; Mechanics, Applied; Structural analysis (Engineering); Periodicals; 624.18
• DOI: 10.1016/j.ijsolstr.2017.08.026
• Languages: English
• ISSNs: 0020-7683

Abstract

Highlights: Simulations and experiments of high energy absorption aluminum extrusions. Systematic study of incorporating anisotropic yield functions into modeling crush. Influence of anisotropy on size optimization and final geometry are explored. Analysis of unconstrained and constrained optimization convergence histories. Abstract: Using advanced constitutive models in simulation tools can improve predictive capabilities of automotive structures in crashworthiness applications. This work presents the influences of coupling anisotropic yield functions into the size optimization of extruded front rails to maximize energy absorption characteristics. Finite element simulations of the extrusion crush response are performed using the von Mises (1913), Hosford (1972) and Barlat et al. (2005) Yld2004-18p yield functions. Each yield function is implemented into a 2-dimensional plane stress and 3-dimensional element formulation to highlight the modeling differences prior to optimization. The simulations are also compared to the experimental dynamic crush response of the extrusion. The response surface methodology (RSM) with the artificial neural network (ANN) metamodeling technique is coupled with the genetic algorithm (GA) optimization scheme to improve the specific energy absorption (SEA) through maximizing energy absorption and minimizing mass. A constrained and unconstrained mass optimization study is performed to identify the sensitivity of global optimization to yield surface choice. Analytical models are derived to explain the influence of the yield surface on the convergence of optimization. The results highlight the importance of incorporating anisotropic yield functions into the optimization process.