The influence of defects on the elastic response of lattice structures resulting from additive manufacturing

Jiang, Panwei; Meter, Edward C. De; Basu, Saurabh

doi:10.1016/j.commatsci.2021.110716

Cited by 20 publications

(6 citation statements)

References 28 publications

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“…In previous studies that focused on indentation of Inconel 718 [18,62], the authors explored the combined effect of surface roughness and void inhomogeneities on the mechanics of indentation. Using numerical simulations, the authors have also realized that the nature of the inhomogeneities characterized by their shape, size, aspect ratio [63], and location within the part geometry [64] complicate the mechanical response of components. However, these variables are not tested in the context of bead-blasting in this study.…”

Section: Effect Of Local Surface Texture On Residual Stresses Resulti...mentioning

confidence: 99%

Effect of Prior Surface Textures on the Resulting Roughness and Residual Stress during Bead-Blasting of Electron Beam Melted Ti-6Al-4V

et al. 2022

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The finishing of additive manufactured (AM) components is crucial for endowing them with fatigue resistance. Unfortunately, current AM processes naturally promote anisotropic surface characteristics that make it challenging to optimize finishing processes. In this study, bead-blasting is explored as a process for finishing Electron Beam Melted (EBM) Ti-6Al-4V. The effects of anisotropic roughness characteristics on the mechanics of bead-blasting are delineated using surface texture measurements via optical profilometry and residual stress measurements via X-ray diffraction. As-received surfaces resulting from AM, as well as those that have been Electrical Discharge Machined (EDM), are studied. It is seen that pre-processed roughness textures heavily influence the final textures and residual stresses. These linkages are quantified using a plasticity index as the governing metric—a rougher surface features a larger plastic index, which results in comparatively greater evolution of its texture characteristics than a smoother surface after equivalent bead-blasting treatments. The mechanics of this evolution are delineated using energy-controlled indentation as a model representing a single impact in bead-blasting. It is seen that rougher surfaces featuring complex textures in as-received states also produce complex stress states featuring a greater level of locally tensile stresses during indentation compared with smoother surfaces. Approaches to address these complications are proposed that can potentially transform a printed, non-functional surface into one that is optimized for fatigue resistance.

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Section: Effect Of Local Surface Texture On Residual Stresses Resulti...mentioning

confidence: 99%

Effect of Prior Surface Textures on the Resulting Roughness and Residual Stress during Bead-Blasting of Electron Beam Melted Ti-6Al-4V

et al. 2022

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“…[21][22][23] Hence, research efforts often focus on enhancing algorithmic efficiency while maintaining optimization quality. Data-driven methods that were tested recently [24][25][26] can produce these enhancements without relying on expensive computations including finite element analysis and sensitivity analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

TO‐NODE: Topology optimization with neural ordinary differential equation

Ma,

De Meter,

Basu

2024

Numerical Meth Engineering

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Deep learning methods have become attractive recently to accelerate topology optimization (TO) because of their capability to save huge computational costs with negligible sacrifice in the quality of final topologies. However, most current approaches rely on numerical mechanics platforms for training which incur significant computation costs. Further, current approaches are not suited for dynamically changing boundary conditions. This is because these deep learning frameworks once trained using results generated with a specific set of boundary conditions do not readily adapt to others. In this article, we present a novel approach to leverage the abilities of deep learning models for TO. We demonstrate that optimization can be achieved using a neural ordinary differential equation. The evolution of the design variable in each iteration of TO is then achieved by numerical integration of this neural differential equation with respect to the starting design. To improve the quality of the results, two levels of generative adversarial networks are also introduced, at the sequence, and image levels, respectively. The proposed machine learning framework is capable of generating full optimization paths relevant to TO in high resolution within seconds and can address novel unseen boundary conditions.

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“…38 For example, in the review paper, 36 the authors pointed out the importance of accounting for the influence of defects in numerical models ("the incorporation of potential imperfections is a must to develop a model that reflects the actual response of a cellular structure") and that micro-Computed Tomography (micro-CT) inspection is the most effective technique for assessing the defect population, despite its high cost. In Jiang et al, 39 circular defects, with radius in the range μm and sinusoidal surface roughness, are simulated in inclined struts of body centered cubic (BCC) lattice structures. The numerical results showed an exponential degradation of the mechanical properties, within thinner struts characterized by a more accentuated degradation.…”

Section: Introductionmentioning

confidence: 99%

Absorbing capabilities of additively manufactured lattice structure specimens for crash applications: Damage tolerant design and simulations

Tridello,

Boursier Niutta,

Benelli

et al. 2024

Fatigue Fract Eng Mat Struct

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In the present work, the influence of defects on the compressive response of octet‐truss AlSi10Mg lattice structure specimens produced with a selective laser melting process is investigated. The defect population in one cell, in two cells, and cubic specimens composed of 27 cells has been assessed with micro‐computed tomography (micro‐CT) analyses. The statistical distributions of the characteristic defect sizes, i.e., the equivalent diameter, the volume, and the surface, assessed in the lattice structure specimens and in volumes randomly extracted from a rectangular bar have been compared. Finally, the compressive behavior of lattice structure specimens has been simulated with a simplified damage‐tolerant finite element model accounting for the influence of defects and compared with experimental results. The analyses have proven that the defect population in volumes extracted from a rectangular bar can provide reliable simulated results, even if micro‐CT inspections of a unit cell or specimens made of several cells are suggested.

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The influence of defects on the elastic response of lattice structures resulting from additive manufacturing

Cited by 20 publications

References 28 publications

Effect of Prior Surface Textures on the Resulting Roughness and Residual Stress during Bead-Blasting of Electron Beam Melted Ti-6Al-4V

Effect of Prior Surface Textures on the Resulting Roughness and Residual Stress during Bead-Blasting of Electron Beam Melted Ti-6Al-4V

TO‐NODE: Topology optimization with neural ordinary differential equation

Absorbing capabilities of additively manufactured lattice structure specimens for crash applications: Damage tolerant design and simulations

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