Abstract
The purpose of the paper is to create a control system of edge quality under finishing machining of parts by elastic polymer abrasive tools, which are effective in surface conditioning and rounding of part edges. The principle of the system approach is used for a formalized description of the system characterized by the mutual interaction between the subsystems that form it. Consideration is given to the control system of these finishing operations design in order to ensure the required quality (in terms of surface roughness, size and geometric accuracy of edges) and optimal process performance under a large variety of tool designs and shapes of the processed surfaces and their mutual arrangement. The system includes input parameters, state space (subroutines) and output data. The input parameters of the system include equipment, workpiece and tool. The state space includes subsystems of mathematical interaction models of tool and surface and forces on the basis of which the mathematical models of material removal, roughness formation, power consumption, tool wear, and cutting zone temperature operate. The information from these subsystems is transmitted to the subsystem for optimizing finishing operation parameters. An algorithm for designing finishing technological operations is presented. It consists of source data input to the system, analysis of operation functional capability, decisions on the possibility of changes in the input data, control action formation, designing of operation, organization of preparation for operation execution, control of operation execution results for the compliance with the requirements of normative and technical documentation and finishing operation implementation in mass production. The developed system provides optimal tools and processing modes as a result of fulfillment of its functions. When this information is implemented in the manufacturing process of parts, the required quality indicators (surface roughness, size and geometric accuracy of edges) are provided as well as the optimal process performance.
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