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Development of software for self-learning technological system

Uncertainty at maintaining the prescribed quality of the machine component functional surfaces is associated with a unique and generalized dependence between the available original cutting data of the controlled object and the required quality of the machined surface. The most effective approach to resolve such uncertainty is in process system learning or self-learning at the control based on the accumulated data. This work contains description of the self-learning process system architecture. The self-learning process system should support the prescribed surface quality of machine components during machining. The structure of the self-learning process system includes the process system itself presented by a numerically controlled cutting machine tool, a sensor gauging the cutting power tangential component, a sensor capturing the temperature of the cutting zone, a sensor metering the roughness parameter and an integration controller. A special attention is paid to the system algorithmic support and software at development of the systems for automated control of the process equipment based on PCs and numerical control machines. Therefore, the operation algorithm and the software are considered as an integral part of the entire self-learning process system. The problem of development of software for self-learning process system is under consideration. The software structure is being developed; interaction of the system basic and hardware software is described. Designing the basic system software modules and functionality of the hardware software are described. The designed software enables creating a self-learning cutting machine complying with the prescribed surface quality of the machined parts.