RT Journal Article
T1 Beam formulation and FE framework for architected structures under finite deformations
A1 Perez Garcia, Carlos
A1 Aranda Ruiz, Josué
A1 Zaera, Ramón
A1 García González, Daniel
AB The breakthrough in additive manufacturing (AM) techniques is opening new routes into the conceptualisation of novel architected materials. However, there are still important roadblocks impeding the full implementation of these technologies in different application fields such as soft robotics or bioengineering. One of the main bottlenecks is the difficulty to perform topological optimisation of the structures and their functional design. To help this endeavour, computational models are essential. Although 3D formulations provide the most reliable tools, these usually present very high computational costs. Beam models based on 1D formulations may overcome this limitation but they need to incorporate all the relevant mechanical features of the 3D problem. Here, we propose a mixed formulation for Timoshenko-type beams to consistently account for axial, shear and bending contributions under finite deformation theory. The framework is formulated on general bases and is suitable for most types of materials, allowing for the straightforward particularisation of the constitutive description. To prove validity of the model, we provide original experimental data on a 3D printed elastomeric material. We first validate the computational framework using a benchmark problem and compare the beam formulation predictions with numerical results from an equivalent 3D model. Then, we further validate the framework and illustrate its flexibility to predict the mechanical response of beam-based structures. To this end, we perform original experiments and numerical simulations on two types of relevant structures: a rhomboid lattice and a bi-stable beam structure. In both cases, the numerical results provide a very good agreement with the experiments by means of both quantitative and qualitative results. Overall, the proposed formulation provides a useful tool to help at designing new architected materials and metamaterial structures based on beam components. The framework presented may open new opportunities to guide AM techniques by feeding machine learning optimisation algorithms.
PB Elsevier
SN 0997-7538
YR 2022
FD 2022-11
LK https://hdl.handle.net/10016/36543
UL https://hdl.handle.net/10016/36543
LA eng
NO The authors acknowledge support from Ministerio de Ciencia e Innovacion MCIN/AEI/10.13039/501100011033 under Grant number PID2020-117894GA-I00, and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP). DGG acknowledges support from the Talent Attraction grant (CM 2018 - 2018-T2/IND-9992) from the Comunidad de Madrid. JAR acknowledges support from the Programa de Apoyo a la Realización de Proyectos Interdiscisplinares de I + D para Jóvenes Investigadores de la Universidad Carlos III de Madrid and Comunidad de Madrid, Spain (project: OPTIMUM).
DS e-Archivo
RD 30 jun. 2024