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A method for design for additive manufacturing rules formulation through Spatio-temporal process discretization

Article dans une revue avec comité de lecture
Author
DOUIN, Chloe
ccGRUHIER, Elise
KROMER, Robin
ccCHRISTMANN, Olivier
211916 Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
ccPERRY, Nicolas
1002421 Institut de Mécanique et d'Ingénierie [I2M]

URI
http://hdl.handle.net/10985/22518
DOI
10.1016/j.procir.2022.05.282
Date
2022-04
Journal
Procedia CIRP

Abstract

Additive Manufacturing (AM) has many advantages, but the lack of access to the knowledge associated with it minimises its development industry. The design phase is crucial for the success of AM, a challenge for Design for Additive Manufacturing (DfAM) methods is therefore to facilitate the access and manipulation of this knowledge. This transfer of knowledge can be achieved by formalising rules at all scales, and communicating them to the designer at the appropriate phase. It is hence necessary to find a way to formalise information in time, space and space-time dimensions since AM is a process that places material in space and layer by layer. The concept of mereotopology is used to study the relationships of connection and interaction between parts, wholes and boundaries, and may be a suitable resource to study DfAM along these three dimensions. The aim of this paper will therefore be to present a method for searching and formulating design guidelines based on a discretisation of the process enabled by the concept of mereotopology. This method consists in the decomposition of a 3D model into features between which spatial, temporal and spatio-temporal interactions are studied. Simultaneously, the analysis of manufactured defects on a printed version of the model allows to link manufacturing defects with a configuration of spatial and temporal elements. Once the defects and configurations have been linked, rules are formulated and then validated or invalidated according to their recurrence on different models printed with the same process and material. This method could be integrated in industry to take advantage of manufacturing defects in order to add data to the statistical study.

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  • Institut de Mécanique et d’Ingénierie de Bordeaux (I2M)
  • Laboratoire Angevin de Mécanique, Procédés et InnovAtion (LAMPA)

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