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http://hdl.handle.net/10553/121987
Title: | 3D modelling strategies to predict the mechanical behaviour of polymeric scaffolds by additive manufacturing | Authors: | Monzón Verona, Mario Domingo Vega Rodríguez, Gisela Del Carmen Paz Hernández, Rubén Gleadall, Andy Alemán-Domínguez, Maria Elena |
UNESCO Clasification: | 3313 Tecnología e ingeniería mecánicas | Issue Date: | 2022 | Journal: | Tissue Engineering - Part A | Conference: | 6th World Congress of the Tissue Engineering and Regenerative Medicine International Society (TERMIS 2021) | Abstract: | Additive Manufacturing (AM) is a well known method to produce scaffolds with different geometrical patterns (grid, honeycomb, gyroid, etc.). These hierarchical porous structures can be designed ad hoc to enable specific mechanical properties and functionalities to provide cell viability and tissue regeneration. In particular, the AM technology based on material extrusion (MEX, commonly known as FDM) produces 3D porous structures processing several thermoplastic biopolymers. Nevertheless, the operational parameters and the characteristics of the deposition process generate a part in which real geometry is different from the theoretical one of the 3D model. This difference involves an additional problem for simulating the mechanical behaviour of the scaffold, for example by Finite Element Analysis (FEA). The paper presents two developed methods to predict the real geometry of scaffolds made by MEX and starting from G-code files. The first method is the ‘‘automated sweep CAD modeller of extrusion-based G-code (DECODE)’’ and the second the ‘‘volume conserving model for 3D printing (VOLCO)’’. DECODE reads the G-code and generates several scripts to automate the 3D CAD modelling (sweep features) to reproduce the extrusion paths. VOLCO builds a voxel-based model starting from the G-code and considering some features in the deposition process. Both methods were compared in terms of volume of the resulting geometry of the scaffolds, and the FEA simulation and testing of the stiffness. DECODE obtains more accuracy in volume (compared to the theoretical models) and can manage larger parts, while VOLCO, after a fitting process, is more accurate for mechanical prediction by FEM. | URI: | http://hdl.handle.net/10553/121987 | ISSN: | 1937-3341 | DOI: | 10.1089/ten.tea.2022.29025.abstracts | Source: | Tissue Engineering - Part A [ISSN 1937-3341], v. 28 (S1), Abstract 84, S-23, (Abril 2022) |
Appears in Collections: | Actas de congresos |
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