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Influence of TPU Lattice Structures on Functional Properties for the Design of Customized Rehabilitation Products

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URI: http://hdl.handle.net/10498/40174

DOI: /10.1007/978-3-031-15928-2_80

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Autor/es
Rosa Silva, Sergio de laAutoridad UCA; Mayuet Ares, Pedro FranciscoAutoridad UCA; Pardo Vicente, Miguel ÁngelAutoridad UCA; Rodríguez Parada, María LucíaAutoridad UCA
Fecha
2023
Departamento/s
Ingeniería Mecánica y Diseño Industrial
Fuente
En: Gerbino, S., Lanzotti, A., Martorelli, M., Mirálbes Buil, R., Rizzi, C., Roucoules, L. (eds). Advances on Mechanics, Design Engineering and Manufacturing IV. JCM 2022. Lecture Notes in Mechanical Engineering. Springer
Resumen
The advantages of AM (Additive Manufacturing) to manufacture complex geometries and custom flexible structures (shape, density, geometry etc.) provides the possibility to use the elastic properties of different materials in order to obtain elastic profiles that could be adapted to a specific customized application. Recent studies established that manufacturing and geometrical parameters like unit cell size and topology, have a defined influence on the stiffness of complex structures like lattice structures, all due to the variation in the volume of material used. This highlights the possibility of designing elastic behavior of structures that could be implemented, for example, in rehabilitation programs which usually use elastic products with highly specific levels of resistance. Therefore, the objective of this works focuses on evaluating the use of the statistical treatment of the data as a guide to predict the behavior of the structures for the design of customized rehabilitation products. A case study based on the use of contour plots as a prediction tool have been carried out. Several TPU lattice specimens were modelled and tested by a simulation of a compression process using finite element analysis (FEA) tools. The reaction force against compression process of several lattice specimens were obtained, showing that the values were within the range of values predicted by the contour plot, validating the prediction. In the same way, the prediction capability with non-studied parameter values, and using a lower number of parameters at the same time were analysed, getting favourable results.
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Esta obra está bajo una Licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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