Publisher DOI: 10.1016/j.compositesb.2019.107193
Title: Multi-scale identification of the elastic properties variability for composite materials through a hybrid optimisation strategy
Language: English
Authors: Cappelli, Lorenzo 
Balokas, Georgios  
Montemurro, Marco 
Dau, Frédéric 
Guillaumat, Laurent 
Issue Date: 1-Nov-2019
Source: Composites Part B: Engineering (176): (2019-11-01)
Journal or Series Name: Composites Part B: Engineering 
Abstract (english): The problem of the identification of the variability characterising the elastic properties of the constitutive phases of a composite (at the microscopic scale) is addressed in this work. To this purpose, the information contained into the probability distribution of the first buckling load of a macroscopic composite specimen is considered, in order to develop a multi-scale identification strategy (MSIS). The goal of the proposed MSIS is achieved by solving an inverse problem: the minimisation of the distance between the numerical and the reference buckling response of the plate, at the macroscopic scale, in terms of statistical moments. Furthermore, thermodynamic constraints are considered to ensure the positive definiteness of the stiffness tensor of each constituent of the composite. The proposed strategy relies on: (a) a semi-analytical homogenisation method, to perform the microscopic / mesoscopic scale transition; (b) the Monte-Carlo technique and an Artificial Neural Network to determine the material properties variability; (c) a general hybrid optimisation algorithm able to deal with optimisation problems defined over a domain of variable dimension to perform the solution search. The effectiveness of the MSIS is proven through two meaningful benchmarks.
URI: http://hdl.handle.net/11420/3075
ISSN: 1879-1069
Institute: Strukturoptimierung im Leichtbau M-EXK1 
Type: (wissenschaftlicher) Artikel
Funded by: This research work has been carried out within the project FULLCOMP (FULLy analysis, design, manufacturing, and health monitoring of COMPosite structures), funded by the European Union Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement No. 642121 .
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