The production of novel types of complex shapes is nowadays enabled by new manufacturing paradigms such as additive manufacturing, also known as 3D printing. The continuous increase of shape complexity imposes new challenges in terms of inspection, product qualification and process monitoring methodologies. Previously proposed methods for 2.5D free-form surfaces are no longer applicable in the presence of this kind of new full 3D geometries. This paper aims to tackle this challenge by presenting a statistical quality monitoring approach for structures that cannot be described in terms of parametric models. The goal consists of identifying out-of-control geometrical distortions by analyzing either local variations within the part or changes from part to part. The proposed approach involves an innovative solution for modeling the deviation between the nominal geometry (the originating 3D model) and the real geometry (measured via x-ray computed tomography) by slicing the shapes and estimating the deviation slice by slice. 3D deviation maps are then transformed into 1D deviation profiles enabling the use of a profile monitoring scheme for local defect detection. The feasibility and potential of this method are demonstrated by focusing on a category of complex shapes where an elemental geometry regularly repeats in space. These shapes are known as lattice structures, or metamaterials, and their trabecular shape is thought to provide innovative mechanical and functional performance. The performance of the proposed method is shown in real and simulated case studies.

Complex geometries in additive manufacturing: A new solution for lattice structure modeling and monitoring

Colosimo B. M.;Grasso M.;Garghetti F.;
2021-01-01

Abstract

The production of novel types of complex shapes is nowadays enabled by new manufacturing paradigms such as additive manufacturing, also known as 3D printing. The continuous increase of shape complexity imposes new challenges in terms of inspection, product qualification and process monitoring methodologies. Previously proposed methods for 2.5D free-form surfaces are no longer applicable in the presence of this kind of new full 3D geometries. This paper aims to tackle this challenge by presenting a statistical quality monitoring approach for structures that cannot be described in terms of parametric models. The goal consists of identifying out-of-control geometrical distortions by analyzing either local variations within the part or changes from part to part. The proposed approach involves an innovative solution for modeling the deviation between the nominal geometry (the originating 3D model) and the real geometry (measured via x-ray computed tomography) by slicing the shapes and estimating the deviation slice by slice. 3D deviation maps are then transformed into 1D deviation profiles enabling the use of a profile monitoring scheme for local defect detection. The feasibility and potential of this method are demonstrated by focusing on a category of complex shapes where an elemental geometry regularly repeats in space. These shapes are known as lattice structures, or metamaterials, and their trabecular shape is thought to provide innovative mechanical and functional performance. The performance of the proposed method is shown in real and simulated case studies.
2021
3D printing
additive manufacturing
complex shape
lattice structure
profile monitoring
SPC
statistical quality monitoring
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1179827
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