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In this study, a new method for identifying the key indicators and dependencies in processing images of helical surfaces of conical cutters at the main stages of the production cycle, which is based on the relationships between the shape of the cutting edges, the angle of inclination of the helical flute, clearance angle, color intensity and image brightness, has been developed. Design and geometric parameters were exported from the simulations with the model created by the automated system for designing conical cutters, which determines the dimensions, shape and geometric parameters of the cutting part of the tool. The identified dependencies allow determining the active zone and the transfer coefficient at the interface between the rear surface and the measuring machine and a stable connection is carried out with a group of cutting part parameters obtained from the results of the CAD system. A high-resolution image of the flank surface boundary zone is scanned with an optical camera on a specialized machine, and the improved method was used to quantitatively calculate the boundary The controlled measurements of a set of point coordinates and geometric parameters of the helical surface of a conical cutter forms a system for a comprehensively studying the designs of conical cutters and the physical mechanisms of their production using image analysis based on the developed indicator interpretation system, which is a foundation for a unified digital cyber-physical production system to be developed. The combination of improved performance or expanded functional capabilities with increased rigidity and reliability, which enables the processing of a wider range of structural designs of part surfaces, is the primary competitive advantage of the new generation of cutting tools with unique working surface geometries. These tools are designed and manufactured using a developed cyberphysical system from contemporary tool materials. With the least amount of frames, the new method's application enables the quickest possible identification of findings that are appropriate for monitoring the back surface of the cutting tool class under consideration.
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