Prediction of Mechanical Properties and Stress–Strain Relation of Closed-Cell Aluminium Foam Under Compression Using Neural Network Models

The presented research aims to find a data-driven formula for the compressive stress–strain behaviour of closed-cell aluminium foams with respect to the apparent density of the material. This is a continuation and new development of an earlier study by the authors. In the previous step, 500 artificial neural network models were built and trained on experimental results from compression tests and then evaluated based on, among other factors, mean absolute relative errors for training and verification stages. In this step, the evaluation of networks is amended, and criteria are introduced that are connected with the mechanical characteristics of the material, i.e., the plateau stress and quasi-elastic gradient. A weighted condition of all measures is proposed. Based on the amended conditions, a neural network model with a weighted mean absolute relative error of ≅5% is chosen and presented, together with the mathematical equation for its stress–strain–density relationship σ = f(ε, ρ) over a range of material apparent densities ρ ∈ <0.2; 0.3> g/cm³. Experimental relationships for compressive strength and plateau stress are also presented.