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Advanced Finite Element Analysis of a Lightweight Nanometal-Polymer Hybrid Component with Experimental Validation, and Its Applications to Vehicle Lightweighting
Technical Paper
2018-01-0152
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The presence of engineering plastics in the automotive, aerospace, and defense industries is rapidly increasing; the lightweight and cost-effective nature of these materials, coupled with improvements to their mechanical performance, is driving the replacement of more traditional materials. However, the stiffness of engineering plastics cannot rival that of their metal counterparts, making metal replacement challenging in cases where stiffness is paramount. Nanometal-polymer hybrids, which are engineering plastics reinforced by a thin high-strength metal coating, provide an innovative solution to this problem. However, implementing this hybrid material into innovative designs remains a challenge, as relatively little information about mechanical behaviour or appropriate modeling techniques for this complex material are available. In this article, an efficient and effective finite element modeling approach for the structural analysis of nanometal-polymer hybrids is presented. The modeling approach is then utilized to assess the performance of a component which is under consideration for metal replacement by nanometal-polymer hybrid material. The geometric and modeling complexity of the component is representative of that found in typical engineering design environments. The results of the finite element model were assessed and validated through direct comparison to experimental data, and demonstrate that the proposed modeling approach provides a level of high degree of accuracy suitable for practical engineering applications.
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Ryan, L., Wong, J., Pitre, R., Faragalli, M. et al., "Advanced Finite Element Analysis of a Lightweight Nanometal-Polymer Hybrid Component with Experimental Validation, and Its Applications to Vehicle Lightweighting," SAE Technical Paper 2018-01-0152, 2018, https://doi.org/10.4271/2018-01-0152.Data Sets - Support Documents
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