Incorporating defects into model predictions of metal lattice-structured materials

Carlton, Holly D.; Volkoff-Shoemaker, Nickolai; Messner, Mark C.; Barton, Nathan R.; Kumar, Mukul

doi:10.1016/j.msea.2021.142427

Cited by 12 publications

(3 citation statements)

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“…Because of the limitations of additive manufacturing, 3D-printed lattice structures usually include defects such as cracks and micro-porosity in struts [ 37 , 38 , 39 , 40 , 41 , 42 ]. Furthermore, printed struts with different tilt angles in the structure can exhibit different mechanical properties [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Properties of Lattice Structures with a Central Cube: Experiments and Simulations

Guo,

Ma,

Liu

et al. 2024

Materials

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In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure by adding a cubic truss in the center of the BCC structure and denoting it TLC (truss–lattice–cube). The different dimensions of the central cube can notably affect the mechanical properties of the lattice structure. With a fixed length (15 mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of polylactic acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure are analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure were significantly improved. The initial flow stress of the TLC increased by 122% at a strain of 0.1; the specific strength enhanced by 293% at a strain of 0.5; and the specific energy absorption improved by 312% at a strain of 0.6. Printing defects in the lattice structure may remarkably damage its mechanical properties. In this work, incorporation of microcracks into the finite element model allows the simulation to capture the influence of printing defects and significantly improve the predictive accuracy of the simulation.

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Section: Introductionmentioning

confidence: 99%

Mechanical Properties of Lattice Structures with a Central Cube: Experiments and Simulations

Guo,

Ma,

Liu

et al. 2024

Materials

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“…The second correction factor, on the other hand, accounts for the influence of internal porosities, by analyzing the volume of defects. In Carlton et al, 41 micro‐CT inspections are carried out to assess the variability of mechanical properties due to defects, focusing on the deviation of the strut diameter. An interesting result is that the failure mode changes depending on the defect density, i.e., a localized failure followed by crushing for lattice structures with a high density of defects and a 45° oriented failure in specimens with small defect density.…”

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 development of each new model with the use of artificial intelligence to predict material data and its verification using a physical experiment provides a great contribution to the development of the field. The articles [7][8][9][10][11] present application of analytical electron microscopy and tomographic techniques to perform the qualitative and quantitative characterization of structural elements in the casts which are then analyzed for classification or/and an evaluation of quality products.…”

Section: Introductionmentioning

confidence: 99%

Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods

et al. 2022

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This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of the casting process parameters. Special attention should be focused on the fact that automatic classification in the case of ductile iron castings is difficult to perform. The biggest problem in this aspect is the high similarity of the void image, which may be a sign of a defect, and the nodular graphite image. Depending on the parameters, the tests on different photos may look similar. Presented in this article are test scenarios of the module analyzing two-dimensional tomographic images focused on the comprehensive assessment by convolutional neural network models, which are designed to classify the provided image. For the purposes of the tests, three such models were created, different from each other in terms of architecture and the number of hyperparameters and trainable parameters. The described study is a part of the decision-making system, supporting the process of qualitative analysis of the obtained cast iron castings.

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Incorporating defects into model predictions of metal lattice-structured materials

Cited by 12 publications

References 50 publications

Mechanical Properties of Lattice Structures with a Central Cube: Experiments and Simulations

Mechanical Properties of Lattice Structures with a Central Cube: Experiments and Simulations

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

Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods

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