This paper describes, in detail, the research of the influence of the trial run of high-precision reducers on the change of their characterizing parameters with the subsequent determination of the methodology for the identification of their critical parameters and positioning accuracy. The research was carried out on a sample of high-precision reducers during a 48 h run-in with the evaluation of changes in their characterizing parameters. The developed methodology unifies the approach to measuring the static and dynamic properties of high-precision reducers to identify their critical parameters and positioning accuracy. The article also points to the need for the correct implementation of the process for the introduction of the bearing reducer into operation after its incorporation into the relevant equipment with emphasis on improving the monitored critical parameters. The running-in of reducers is a little-explored area in terms of its effect on changing the wide range of characterizing properties of high-precision reducers. At the same time, it is complicated by the non-existent uniform methodology for the implementation of their run-in.

This paper deals with the current topic of computer vision which is used in quality control, accurate measurement or robot guidance. Templates are a key component of template matching algorithms. Creating them is a complex and demanding process. The authors of the paper try to describe the possibilities of automated template generation from information that is already available about the product. A digital version in the CAD system exists of virtually every product nowadays. An experiment has shown that with the use of this information, it is possible to create a template that will replace templates created through standard procedures.

Modern CNC machine tools include a number of sensors that collect machine status data. These data are used to control the production process and for control of the CNC machine status. No less important part of the production process is also a machine tool. The condition of the cutting tool is important for the production quality and its failure can cause serious problems. Monitoring the condition of the cutting tool is complicated due to its dimensions and working conditions. The article describes how the tool wear can be predicted from the measured values of vibration and pressure by using neural networks.