In this article actual topics concerned with mechanical properties of bulk materials, usage of computer vision and artificial neural networks in this research are discussed. The main principles of the system for analysis of bulk materials mechanical characteristics are described. Bulk material outflow behaviour with predefined parameters (particles shapes and radius, coefficients of friction, etc.) was modelled. The outflow was modelled from the calibrated conical funnel. Obtained dependencies between mechanical characteristics and pile geometrical properties are represented as diagrams and graphs.

The problem of DEM models calibration is considered in the article. It is proposed to divide models input parameters into those that require iterative calibration and those that are recommended to measure directly. A new method for model calibration based on the design of the experiment for iteratively calibrated parameters is proposed. The experiment is conducted using a specially designed stand. The results are processed with technical vision algorithms. Approximating functions are obtained and the error of the implemented software and hardware complex is estimated. The prospects of the obtained results are discussed.

This paper presents a numerical simulation of powder sintering. The numerical model presented in this paper uses the discrete element method, which suggests that the material can be modeled by a large set of discrete elements (particles) of a spherical shape that interact with each other. A methodology has been developed to determine the DEM parameters of bulk materials based on machine vision and a neural network algorithm. The approach is suitable for obtaining the exact values of the DEM parameters of the investigated bulk material by comparing the visual images of the material’s behavior at the experimental stand in reality and in the model. Simulation of sintering requires an introduction of cohesive interaction between particles representing interparticle sintering forces. Numerical sintering studies were supplemented with experimental studies that provided data for calibration and model validation. The experimental results have shown a significant capability of the designed numerical model in modeling sintering processes. Evolution of microstructure and density during sintering have been studied under the laboratory conditions.