As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.

On-line inspection of lattice structures and metamaterials via in-situ imaging in additive manufacturing

Colosimo B. M.;Garghetti F.;Grasso M.;Pagani L.
2024-01-01

Abstract

As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.
2024
Additive manufacturing
Data mining
Image
In-situ monitoring
Inspection
Lattice structures
Metamaterials
Powder bed fusion
Qualification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1277729
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