Topologically Optimized Graded Foams

Iaccarino, Paolo; Maresca, Elvira; Morganti, Simone; Auricchio, Ferdinando; Di Maio, Ernesto

doi:10.1002/adem.202301798

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The graded-density sample was produced as detailed in Iaccarino et al 6 . Briefly, preforms were produced by assembling 1 mm-thick slabs cut from PP sheet, compression molded as previously described.…”

Section: Methodsmentioning

confidence: 99%

“…Typically, plastic foams have uniform density and morphology, but graded foams and foams with a 3D density map are gaining attention in science and various technical fields 5 , 6 . In both cases, understanding if a required density map is actually obtained after the production process is still an open technical issue.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, by means of the calibration curve, a quantitative description of their non-uniform density pattern, invisible to visual inspection, is obtained. Finally, the approach is exploited to image the density pattern of a foam sample having a 3D optimized density map for the three-point bending load condition 6 . The density map achieved via THz is validated with the one resulting from X-Ray microscopy.…”

Section: Introductionmentioning

confidence: 99%

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Foam density mapping via THz imaging

Catapano,

Zappia,

Iaccarino

et al. 2024

Sci Rep

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Plastic foams, near-ubiquitous in everyday life and industry, show properties that depend primarily on density. Density measurement, although straightforward in principle, is not always easy. As such, while several methods are available, plastic foam industry is not yet supported with a standard technique that effectively enables to control density maps. To overcome this issue, this paper proposes Terahertz (THz) time-of-flight imaging using normal reflection measurements as a fast, relatively cheap, contactless, non-destructive and non-dangerous way to map plastic foam density, based on the expected relationship between density and refractive index. The approach is demonstrated in the case of polypropylene foams. First, the relationship between the estimated effective refractive index and the polypropylene foam density is derived by characterizing a set of carefully crafted samples having uniform density in the range 70–900 kg/m3. The obtained calibration curve subtends a linear relationship between the density and the refractive index in the range of interest. This relationship is validated against a set of test samples, whose estimated average densities are consistent with the nominal ones, with an absolute error lower than 10 kg/m3 and a percentage error on the estimate of 5%. Exploiting the calibration curve, it is possible to build quantitative images depicting the spatial distribution of the sample density. THz images are able to reveal the non-uniform density distribution of some samples, which cannot be appreciated from visual inspection. Finally, the complex spatial density pattern of a graded foam sample is characterized and quantitatively compared with the density map obtained via X-ray microscopy. The comparison confirms that the proposed THz approach successfully determines the density pattern with an accuracy and a spatial scale variability compliant with those commonly required for plastic foam density estimate.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Foam density mapping via THz imaging

Catapano,

Zappia,

Iaccarino

et al. 2024

Sci Rep

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Drop‐Weight Impact Resistance of 3D‐Printed Complex Zeolite‐Inspired Structures

Ambekar,

Oliveira,

Pugazhenthi

et al. 2024

Adv Eng Mater

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Ballistic resistance architectures have crucial importance in the defense and aerospace industries. The researchers are constantly excited to explore lightweight, complex architectures for higher mechanical energy absorption. The complexity of the design is restricted due to a lack of advanced manufacturing techniques. However, additive manufacturing (AM)/3D printing facilitates sustainability, excellent design flexibility, and automation. The zeolite‐inspired 3D printed structures are designed and developed to study deformation under low‐velocity drop impact. The present work has the comparison of the specific energy absorption of different types of zeolite‐inspired structures. It also has the effect of velocity on impact depth at multiscale using molecular dynamics (theoretical) and computer tomography (experimental).

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Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms

Bai,

Li,

Shen

2024

Materials

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The yield strength and Young’s modulus of lattice structures are essential mechanical parameters that influence the utilization of materials in the aerospace and medical fields. Currently, accurately determining the Young’s modulus and yield strength of lattice structures often requires conduction of a large number of experiments for prediction and validation purposes. To save time and effort to accurately predict the material yield strength and Young’s modulus, based on the existing experimental data, finite element analysis is employed to expand the dataset. An artificial neural network algorithm is then used to establish a relationship model between the topology of the lattice structure and Young’s modulus (the yield strength), which is analyzed and verified. The Gibson–Ashby model analysis indicates that different lattice structures can be classified into two main deformation forms. To obtain an artificial neural network model that can accurately predict different lattice structures and be deployed in the prediction of BCC-FCC lattice structures, the artificial network model is further optimized and validated. Concurrently, the topology of disparate lattice structures gives rise to a certain discrete form of their dominant deformation, which consequently affects the neural network prediction. In conclusion, the prediction of Young’s modulus and yield strength of lattice structures using artificial neural networks is a feasible approach that can contribute to the development of lattice structures in the aerospace and medical fields.

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Topologically Optimized Graded Foams

Cited by 3 publications

References 41 publications

Foam density mapping via THz imaging

Foam density mapping via THz imaging

Drop‐Weight Impact Resistance of 3D‐Printed Complex Zeolite‐Inspired Structures

Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms

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