Verification of Application of ANN Modelling in Study of Compressive Behaviour of Aluminium Sponges - STRĘK, Anna Małgorzata; DUDZIK, Marek; KWIECIEŃ, Arkadiusz; WAŃCZYK, Krzysztof; LIPOWSKA, Barbara (Engineering Transactions, 2019)
This article presents a preliminary neural network analysis of the compressive behaviour of aluminium open-cell sponges and answers the question of whether this phenomenon can be modelled using artificial intelligence. The research consisted of two phases: first – compression experiments, which in turn provided data for the second phase – the artificial neural network (ANN) analysis. A two-argument function was proposed and tested using the gathered experimental data with a two-layer feedforward network. The determination coefficient R^2 for linear correlation between targets and modelling outputs was chosen as the criterion for the assessment of the quality of modelling. The obtained values were R^2 > 0.96, which shows that neural networks hold the capacity to address the characterisation of the mechanical response of aluminium open-cell sponges in compression. Additionally, the mean absolute relative error (MARE) and the mean square error (MSE) were also determined.