The research considers conducting orthogonal experiment (OT) as one of the stages in developing a new discrete element method (DEM) parameters calibration approach. The measured responses in experiment are the parameters obtained by DEM animation processing using machine vision system (MVS). The variable factors in experiment are DEM parameters. A brief overview of an existing calibration approaches given in the article. The choice of OT as a design of experiment tool among other mathematical tools discussed. Experiments conducted using specially developed rig where bulk material's flow captured as DEM animation. DEM animation converted to video and then processed using MVS that allow register the values of such parameters as angle of repose or expiration time (measured responses). The results of the OT show that it is possible to identify four measured responses with the most valuable correlation coefficient. DEM parameters with the biggest influence on the measured responses identified for each of the obtained regression. Obtained results are useful in learning or iterative algorithms development for DEM parameters calibration.

DEM parameters calibration is the most important step in preparing a DEM model. At the same time, the lack of a universal approach to DEM parameters calibration complicates this process. The paper presents the author’s approach to creating a universal calibration approach based on the physical meaning of the friction coefficients and conducting symmetrical experiments at full scale and in a simulation, as well as the implementation of the approach in the form of a physical test rig. Several experiments were carried out to determine the DEM parameters of six material–boundary pairs. The resulting parameters were adjusted using a refinement experiment. The results confirmed the adequacy of the developed approach, as well as its applicability in various conditions. The limitations of both the approach itself and its specific implementation in the form of a test rig were identified.

A lot of researchers are developing new DEM parameters calibration approaches based on an experiment plan or the use of learning algorithms. This research is aimed at improving iterative algorithms frequently used for calibration. The big time consumption as a main problem of iterative algorithms is questioned. It is proposed to use Random forest algorithm to determine DEM parameters impact on the measured bulk responses. Measured responses are the parameters obtained by image processing using a technical vision system. As a result of 200 experiments processing, DEM parameters impact values on each bulk response were generated and presented as histograms. Obtained results were interpreted on the basis of the bulk material behavior and its physical properties. There is a discussion on the possibility of developing a universal DEM parameters calibration method based on the iterative algorithm.