journal article·2025-04-08

Effect of active cooling on the formation of IN718 microstructures in directed energy deposition additive manufacturing

Michèle Bréhier, Daniel Weisz-Patrault, Christophe Tournier

Rapid Prototyping Journal· Volume 31· Issue 4
Publisher page

Abstract

Purpose This paper aims to focus on laser metal powder-directed energy deposition, which is used to repair parts or manufacture high-performance components. Fine and equiaxed microstructures are often targeted because of their homogeneous mechanical properties. However, doing so can only be done by either adjusting process parameters or using external actuators that necessitate additional equipment. This paper presents a method that circumvents these issues by using only the powder spray nozzle and inerting gas when the laser is switched off to actively cool the part without additional equipment. Design/methodology/approach Single-bead IN718 thin walls were produced with a unidirectional strategy, taking advantage of the return path to actively cool the part. Six different sets of parameters were chosen to cover the operating range of laser power and machine scanning speed. Findings Analysis of the electron back-scatter diffraction maps of the walls highlights the impact of this active cooling strategy on the microstructure. A fine, untextured microstructure was observed, regardless of process parameters, which enables optimization of process parameters to maximize productivity instead of being conditioned by the targeted fine-equiaxed microstructure. The grain size obtained can be further refined by increasing the scanning speed of the actively cooled parts. Originality/value An informed choice of off-production nozzle trajectories would enable reaching a fine and untextured microstructure independently of process parameters.
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Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · High Entropy Alloys Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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