The experimental studies results of the mechanical and thermo-mechanical treatment influence of wear resistance of excavator bucket tooth materials and rotary cutting tool are presented in this article. Experiments on abrasion in different rocks were investigated. In the example of 110G13L steel (Hadfield steel), it was shown that mechanical strengthening (cold hardening) is an effective way to improve wear resistance of machine parts working in abrasive environments. It was revealed that if the hardness of abrasive materials is lower than the hardness of cold hardening steel, wear resistance may increase by 10 times and if the hardness of abrasive materials is higher than the hardness of steel with cold hardening, it does not influence wear resistance. It was found that the high-temperature thermo-mechanical treatment of 30KhGSA steel increases its hardness (by 23%) and wear resistance (by 38%) by comparing with the same steel after hardening. It was concluded that the advisable use one of these methods depends on rock hardness.

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.

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.