Methodology for Experimental Investigations of Metal Foams and Their Mechanical Properties

Stręk, Anna

doi:10.7494/mech.2012.31.2.90

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Taking into consideration so much engineering and design interest in closed-cell aluminium foams, it is a natural consequence that much scientific attention is drawn to the appropriate description of this material in various aspects. A significant number of works focus on structural characterization, e.g., [ 16 , 29 , 30 , 31 ], property analysis, e.g., [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 32 ], manufacturing methods, e.g., [ 12 , 16 , 33 ], experimental investigations, e.g., [ 34 , 35 , 36 , 37 , 38 , 39 ] and modelling. As for modelling, this field is widely researched, and the number of publications about this subject is extensive.…”

Section: Introductionmentioning

confidence: 99%

Specifications for Modelling of the Phenomenon of Compression of Closed-Cell Aluminium Foams with Neural Networks

2022

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The article presents a novel application of the most up-to-date computational approach, i.e., artificial intelligence, to the problem of the compression of closed-cell aluminium. The objective of the research was to investigate whether the phenomenon can be described by neural networks and to determine the details of the network architecture so that the assumed criteria of accuracy, ability to prognose and repeatability would be complied. The methodology consisted of the following stages: experimental compression of foam specimens, choice of machine learning parameters, implementation of an algorithm for building different structures of artificial neural networks (ANNs), a two-step verification of the quality of built models and finally the choice of the most appropriate ones. The studied ANNs were two-layer feedforward networks with varying neuron numbers in the hidden layer. The following measures of evaluation were assumed: mean square error (MSE), sum of absolute errors (SAE) and mean absolute relative error (MARE). Obtained results show that networks trained with the assumed learning parameters which had 4 to 11 neurons in the hidden layer were appropriate for modelling and prognosing the compression of closed-cell aluminium in the assumed domains; however, they fulfilled accuracy and repeatability conditions differently. The network with six neurons in the hidden layer provided the best accuracy of prognosis at but little robustness. On the other hand, the structure with a complexity of 11 neurons gave a similar high-quality of prognosis at but with a much better robustness indication (80%). The results also allowed the determination of the minimum threshold of the accuracy of prognosis: . In conclusion, the research shows that the phenomenon of the compression of aluminium foam is able to be described by neural networks within the frames of made assumptions and allowed for the determination of detailed specifications of structure and learning parameters for building models with good-quality accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Specifications for Modelling of the Phenomenon of Compression of Closed-Cell Aluminium Foams with Neural Networks

2022

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“…The methods for the preparation of the samples were analyzed in [22] taking into consideration the particular use of the experiments to research the cell structure of metallic materials. A lot of information on the physical properties of metallic foams can be collected, and their impact and outcomes can be extracted from literature.…”

Section: Introductionmentioning

confidence: 99%

Effect of the manufacturing parameters on the pore size and porosity of closed-cell hybrid aluminum foams

Ali

Gábora

Naeem

et al. 2021

IRASE

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Over the recent years metallic foams have become a popular material due to their unique characteristics like low density coupled with beneficial mechanical properties such as good energy absorption, heat resistance, flame resistance, etc. However, their production processes (foaming) is highly stochastic which results in an inhomogeneous foam structure. Hybrid aluminum foam with closed-cell has been manufactured using direct foaming method coupled with the Taguchi Design of Experiments (DOE). Image analysis has been carried out to determine the average porous area and pore size. The influence of the production parameters (amount of foaming agent added, mixing speed and temperature) on the pore size and the porous area has been analyzed using the statistical Taguchi technique. From the experiments it was seen that the most important control factor for both the pore size and the porous area is the amount of the foaming agent added, followed by temperature and stirring speed. Furthermore, the statistical significance of these manufacturing parameters on the response was also investigated by performing analysis of variance (ANOVA) statistical method.

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“…This led to various studies in the last decades which were focused on the design and investigation of honeycombs specifically engineered to exhibit anomalous or superior thermomechanical properties, such as a negative Poisson's ratio (auxetic behavior) . Given the advantages of honeycombs over nonporous solids, when put in the context of the developments which are being made on “negative thermomechanical properties,” it is not surprising that research in the field of “negative honeycombs” is still the subject of extensive research …”

Section: Introductionmentioning

confidence: 99%

On the Design of Multimaterial Honeycombs and Structures with T‐Shaped Joints Having Tunable Thermal and Compressibility Properties

Cauchi

Attard

Grima‐Cornish

2020

Physica Status Solidi (b)

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Honeycombs made from more than one material are proposed and analyzed with the help of finite element simulations. It is shown that such honeycombs are designed to change their shape when subjected to a change in temperature or pressure and can be tailor-made to exhibit particular thermomechanical properties. It is also shown that the anomalous properties of negative thermal expansion/ compressibility can be manifested from honeycombs built from common everyday conventional materials. Applications of such constructs are discussed including their role in the manufacture of smart grips which tighten upon heating.

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Methodology for Experimental Investigations of Metal Foams and Their Mechanical Properties

Cited by 6 publications

References 20 publications

Specifications for Modelling of the Phenomenon of Compression of Closed-Cell Aluminium Foams with Neural Networks

Specifications for Modelling of the Phenomenon of Compression of Closed-Cell Aluminium Foams with Neural Networks

Effect of the manufacturing parameters on the pore size and porosity of closed-cell hybrid aluminum foams

On the Design of Multimaterial Honeycombs and Structures with T‐Shaped Joints Having Tunable Thermal and Compressibility Properties

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