A new multi-scale design methodology for the optimisation of variable stiffness composites
TypeCommunications avec actes
In this work a multi-scale two-level (MS2L) optimisation strategy for optimising VAT composites is presented. In the framework of the MS2L methodology, the design problem is split and solved into two steps. At the first step the goal is to determine the optimum distribution of the laminate stiffness properties over the structure (macroscopic scale), while the second step aims at retrieving the optimum fibres path in each layer meeting all the requirements provided by the problem at hand (mesoscopic scale). The MS2L strategy has been improved in order to integrate all types of requirements (mechanical, manufacturability, geometric, etc.) within the first-level problem. The proposed approach relies on: (a) the polar formalism for describing the behaviour of the VAT laminate, (b) the iso-geometric surfaces for describing the spatial variation of both the laminate stiffness properties (macro-scale) and the layers fibres-path (meso-scale) and (c) an hybrid optimisation tool (genetic and gradient-based algorithms) to perform the solution search. The effectiveness of the MS2L strategy is proven through a numerical example on the maximisation of the first buckling factor of a VAT plate subject to both mechanical and manufacturability constraints.
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MONTEMURRO, Marco; CATAPANO, Anita (2016)The present paper focuses on the development of a multi-scale design strategy for the optimisation of variable angle stiffness laminates. The main goal consists in proving that it is possible to design structures having ...
On the effective integration of manufacturability constraints within the multi-scale methodology for designing variable angle-tow laminates MONTEMURRO, Marco; CATAPANO, Anita (Elsevier, 2017)In this work a multi-scale two-level (MS2L) optimisation strategy for optimising VAT composites is presented. In the framework of the MS2L methodology, the design problem is split and solved into two steps. At the first ...
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