DEVELOPMENT OF ALGORITHMIC SUPPORT FOR PRACTICAL IMPLEMENTATION OF TESTING AND DIAGNOSTIC COMPLEX OF CNC MACHINES

Abstract

CNC machines, including multi-purpose machines (product processing centers), have been widely used in connection with the creation of flexible production systems and significant progress in developing and improving the reliability of multi-purpose freely programmable, multiprocessor and small numerical program control devices (CNC) , so the development of algorithmic software for the practical implementation of the test and diagnostic complex and the use of diagnostic systems as a means of automation of information technology is one of the general directions of improving the efficiency of machine-building enterprises. However, in order to make decisions about the scope, stages and feasibility of using a particular diagnostic system for the selected object of study at a particular enterprise, it is necessary to assess its expected benefits. The use of diagnostics during operation of the machine imposes its influence on the means and methods of diagnosis, which should be convenient for use in the factory, to ensure the diagnosis process in the shortest time, to have reliable readings, especially with high requirements for product reliability. and in some cases without disrupting the mechanism, to be economically feasible. The design of the machine is adapted to the needs of diagnostics and must: have built-in devices that evaluate its parameters (pressure in the hydraulic system, temperature of energy-intensive units, accuracy of machining, speed); periodically connect to special equipment that will diagnose the basic parameters of the machine and provide data on its condition. The parameters of the technical condition (diagnostic features), which can be judged on the OD and which are diagnosed during operation of the machine, are: machine parameters that directly characterize its efficiency; damage and defects that occur during operation and lead or may lead to failure; side effects that are functionally or stochastically related to the initial parameters.