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Parametric analysis and machine learning-based parametric modeling of wire laser metal deposition induced porosity

Article dans une revue avec comité de lecture
Author
LOREAU, Tanguy
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
CHAMPANEY, Victor
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
HASCOET, Nicolas
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
LAMBARRI, Jon
207580 Manufacturing Processes Department, FundacioÌn Tekniker-IK4,
MADARIETA, Mikel
207580 Manufacturing Processes Department, FundacioÌn Tekniker-IK4,
GARMENDIA, Iker
207580 Manufacturing Processes Department, FundacioÌn Tekniker-IK4,
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/22196
DOI
10.1007/s12289-022-01687-3
Date
2022-04
Journal
International Journal of Material Forming

Abstract

Additive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, rich metallurgical transformations and combinatorial deposition trajectories, induce numerous process defects and in particular porosity. Simulating numerically porosity appearance remains extremely complex because of the multiple physics induced by the laser-material interaction, the multiple space and time scales, with a strong impact on the simulation efficiency and performances. Moreover, when analyzing parts build-up by using the wire laser metal deposition —wLMD— technology it can be noticed a significant variability in the porosity size and distribution even when process parameters remain unchanged. For these reasons the present paper aims at proposing an alternative modeling approach based on the use of neural networks to express the porosity as a function of different process parameters that will be extracted from the process analysis.

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