Abstract
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.
Highlights
IntroductionClosed-cell aluminium is a well-known engineering material, mostly used where lightweight applications require satisfactory mechanical properties [1–3] or energy absorption as a determinant [2,4]
The main concept of the research stage devoted to neural networks was to generate and train a considerable set of comparable networks, assess them according to assumed criteria and —based on the choice of the ‘best’ network—determine the most adequate neural network structure and learning parameters for building a model of the
The main concept of the research stage devoted to neural networks was to generate and train a considerable set of comparable networks, assess them according to assumed criteria and —based on the choice of the ‘best’ network—determine the most adequate neural network structure and learning parameters for building a model of the phenomenon of closed-cell aluminium compression
Summary
Closed-cell aluminium is a well-known engineering material, mostly used where lightweight applications require satisfactory mechanical properties [1–3] or energy absorption as a determinant [2,4]. Examples of the usage of aluminium foams include, among others: the automotive industry, space industry, energy and battery field, military applications and machine construction [16–20]. We would like to highlight civil engineering and architecture here, since these application fields are unjustly underestimated in the metal foam industry even though they have significant potential. Examples of the usage of closed and open cellular metals include: structural elements (e.g., wall slabs, staircase slabs, parking slabs) [17,21,22], interior and exterior architectural design [23,24], highway sound absorbers [5,25], architectural electromagnetic shielding [26], sound absorbers in metro tunnels [17], dividing wall slabs with sound insulation (e.g., for lecture halls) [27] and the novel concept of earthquake protection against building pounding [28]
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