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.

Optimum Selection of Coated Piston Rings and Thrust Bearings in Mixed Lubrication for Different Lubricants Using Machine Learning

“…The coefficient of friction is used in the y axis as a response value, while the first predictor appears in the x axis, 0.017 units. Similarly, in the case of SAE 10W40, the equation of the prediction model for the coefficient of friction is (38) with 𝑅 = 0.90 and S = 0.015, which means 90% accuracy in prediction of the actual coefficient of friction for the contact with an average error of 0.015 for the estimation. Finally, in the case of AWS 100, the regression model is given by Equation ( 39) with 𝑅 = 0.85 and S = 0.021 or 85% accurate prediction for the friction coefficient in terms of sliding velocity and Young's modulus of the coating with an average deviation from the observations of 0.021.…”

“…Senatore et al [37] found the lower average friction losses of piston ring-liner conjunction utilizing an Artificial Neural Network (ANN) model. Wołowiec-Korecka et al [38] trained a neural network model for the creation of low-frictional coatings on piston ring surfaces. They built an industrial tool to optimize the nitriding process under reduced pressure in the piston ring manufacturing process.…”

Low Frictional MoS2/WS2/FineLPN Hybrid Layers on Nodular Iron

“…After the natural drying, the coated specimens have been annealed at 300 • C for 1 h in an inert N 2 atmosphere to sinter the cladded particles and to adhere them to the metallic substrate. Next, such preliminary prepared green compacts were thermo-chemically treated by FineLPN low-pressure nitriding [31] or alternatively, additionally treated by gas sulphonitriding in an active atmosphere containing ammonia gas and sulphur vapors [35]. Two options A and B of the multi-stage surface engineering process have been conducted and compared.…”

“…More intricate is the issue of cast iron nitriding, due to the presence of graphite precipitation in the microstructure [29,30] Recently, the new non-equilibrium, low-pressure nitriding process (FineLPN) has been developed. It may be used for creation of fully controlled phase structure of nitrided case both on steel and cast iron due to dedicated neural network computer support [31].…”

Algorithm Scheme to Simulate the Distortions during Gas Quenching in a Single-Piece Flow Technology

“…Moreover, more than one modeling program was required. The paper [19] described a neural network model and its training procedures based on data mining in the application, but only to monitor and control low-pressure nitriding process for the creation of low-frictional coatings. The authors [20] presented a numerical model based on the finite element method (FEM), which allows the deformations in the input elements to be determined, but with a step change in the material properties and constant cooling rate parameters.…”