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Computational Optimization of Voids on 3D Woven Composites Truss Structures During Infusion

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Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 283))

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Abstract

Whole world has undertaken a low carbon emission process due to sustainability and the potential for composites to reduce greenhouse gas (CO2) is clear. Therefore, new composites truss structures materials with 3D woven continuous fibre reinforced composites will start to be used for civil, aerospace, automotive, and marine applications due to their lightweight, water resistance, their internal electrical conductivity and superior mechanical properties. The overall goal and attitude of this paper are to predict the fluid flow behaviour during the liquid infusion processes which are one of the most common manufacturing routes for composites and optimize computationally the high concentration of voids that may arise. Since experimentally this work is presented with high complexity and very expensive. The void formation can compromise the truss structure integrity and the final mechanical properties. The following research work tries to deal with the resin flow behaviour during impregnation affected by the preform properties, which are fibres orientation, and textile volume fractions, that can vary locally. Advanced composites truss structures are made of complex geometry which is made off 3D woven geometrically complex preforms, for better through-thickness properties. Thus making the impregnation process hard to control and potentially causing void defects in the manufacturing of the final component. Therefore, three-dimensional computational optimization scenarios are close realistic and can be used in the final manufacturing process of the truss structure.

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Correspondence to Spiridon Koutsonas .

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Koutsonas, S., Haroglu, H. (2022). Computational Optimization of Voids on 3D Woven Composites Truss Structures During Infusion. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_18

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