The paper studies the possibility of using artificial neural networks (ANN) to determine certain mechanical properties of a new composite material. This new material is obtained by a mixture of hemp and polypropylene fibres. The material was developed for the industry of upholstered furniture. Specifically, it is intended for the making of elements of the support structure of some upholstered goods (chairs, armchairs, sofa sides) with the objective of replacing wood. The paper aims to calculate the following mechanical properties: maximum tensile strength and maximum elongation.

The paper presents a method of interpolating bifactorial experimental data with the aim of extending the results over the entire domain defined by the independent variables. This approach can be used in determining the optimum mineral casting mixes for a given application. Mechanical properties such as stiffness, ultimate tensile strength, yield strength and Poisson's coefficient can be improved.The paper describes the implementation of a radial basis function (RBF) algorithm in a MATLAB program for the interpolation of data, plotting of the results and the identification of the maximum dependent variable and the independent variables that correspond to it. It also provides the source code for the program along with explanations regarding its use. RBF interpolations can be used successfully in response surface methodology to generate approximations of the studied variables based on the experimental results. The program can be extended to work on any number of input and output variables.

Abstract. Mineral casting is a type of polymer concrete that has started to be used in commercial machine-tools in the last few years because of its low cost of manufacturing and vibration damping capabilities. The paper presents the modelling and finite element analysis of a representative elementary volume (REV) made of mineral casting subjected to tensile loading. The aim of the paper is to assess the stress distribution that develops inside the specimen in order to understand and improve the material.