Neural computing for a low-frictional coatings manufacturing of aircraft engines’ piston rings

Abstract

The “boost-diffusion” low-pressure nitriding used to low-frictional coatings manufacturing of aircraft engines’ piston rings is a nonsteady-state process; therefore, designing and prediction of the process’ kinetics by analytical solutions of Fick’s equations or numerical methods of diffusion are difficult, due to the nonlinear relationship between the diffusion coefficient and the rate of diffusion as well as nonsteady-state boundary conditions. The best solution in this case, as the practice and theory indicate, is computer-aided design based on neural networks. The paper describes neural network model and its training procedures based on data mining in the application to the monitoring and control of low-pressure nitriding process for creation of low-frictional coatings on gray irons and steels used for the piston rings manufacturing. The goal was to study the usefulness of the multilayer feed-forward perceptrons and radial basis function of neural networks for modeling of multiphase kinetic diffusion for low-pressure nitriding. As it was shown, the use of specialist networks that designate single features gives more accurate prediction results than the use of general networks that design several features at the same time. It has been proved that it is possible to construct an industrial application of the low-pressure nitriding based on artificial neural networks. The results of the research will be the basis for the development of innovative, specialized software supporting the design of gradient low-friction layers based on the FineLPN low-pressure nitriding and consequently the design of intelligent supervision over their manufacturing technology.