Design of a decision support system to form optimal technological processes for parts machining based on artificial intelligence methods

Abstract

The object of this study is the process of designing a decision support system for automating the formation of technological processes (TPs) for machining parts of high-precision equipment for the aviation industry. The task of improving the efficiency of the optimization of the process of mechanical processing of parts through the use of a decision support system (DSS) and artificial intelligence methods, which, unlike known analytical approaches, allow describing processes and phenomena that do not have strict formalization, has been solved. DSS consists of three subsystems. The first is an information subsystem for the automated formation of the structure in the technological process of machining parts of high-precision equipment. The second is an information subsystem for optimizing parameters of TP operations by cutting, taking into account the accumulation of tool wear. The third is a subsystem of control and adjustment of operating parameters. In the process of conducting research, an approach was devised for designing optimal technological processes to machine parts of high-precision equipment. The task of designing the structure of technological processes was solved using production rules. The task of determining the optimal parameters of turning and milling operations was solved in a multi-criteria statement. The following objective functions were used: cost of the operation, specific energy consumption for the operation, and productivity of the operation. At the same time, the wear of the tool accumulated over time was taken into account. The solution was obtained by searching for the Pareto-optimal solution using genetic algorithms and artificial neural networks. As a result of the work of DSS, an optimal technological process for machining parts of high-precision equipment for the aviation industry was formed, which made it possible to reduce the production time of one part by 5 % and reduce the total cost of production of the part by 14 %