Parametric analysis and machine learning-based parametric modeling of wire laser metal deposition induced porosity - Archive ouverte HAL Access content directly
Journal Articles International Journal of Material Forming Year : 2022

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

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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Dates and versions

hal-03708263 , version 1 (29-06-2022)

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Tanguy Loreau, Victor Champaney, Nicolas Hascoet, Jon Lambarri, Mikel Madarieta, et al.. Parametric analysis and machine learning-based parametric modeling of wire laser metal deposition induced porosity. International Journal of Material Forming, 2022, 15 (3), ⟨10.1007/s12289-022-01687-3⟩. ⟨hal-03708263⟩
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