Crash Padding Mechanical Properties and Impact Response Analysis

Orringer, Oscar; Knadle, K T; Mandell, John F.

doi:10.4271/840866

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…These materials are observed in nature in the form of wood, cork, trabecular bone, plant parenchyma, and marine sponges, among others [2]. In engineering, man-made honeycombs and foams are used in a variety of applications raging from coffee cups and lattice injection molds [3] to helmet liners [4] and crash padding in automotive vehicles [5]. Design of cellular materials and (macroscale) cellular structures is gaining relevance due to advances in additive manufacturing and structural optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness

Liu

Detwiler

Tovar

2018

Journal of Mechanical Design

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The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear and nonlinear models.

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Section: Introductionmentioning

confidence: 99%