Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings

Abstract

The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposition method in order to solve the mechanical mismatch problem between substrate and coatings. More importantly, a backward propagation (BP) neural network was applied to efficiently optimize the pulse electrodeposition process of Ni–W graded coating. The SEM, EDS, XRD, Vickers hardness tester and Weighing scales are used to analyze the micromorphology, chemical element, phase composition, and micro hardness as well as oxidation weight increase, respectively. The results show that the optimal process conditions with BP neural network are as follows: the bath temperature is 30 °C, current density is 15 mA/cm2 and duty cycle is 0.3. The predicted value of the model agrees well with the experimental value curve, the relative error is minor. The maximum error is less than 3%, and the correlation coefficient is 0.9996. The Ni–W graded coating prepared by BP neural network shows good bonding with the substrate which has flat and smooth interface. The thickness of the coating is about 136 μm, which slows down the oxidation of the substrate and plays an effective role in protecting the substrate.

Study of ZrO2 Content on Hardness and Wear Characteristic of Coatings

“…As a result, the amount of ZrO 2 particles deposited in the Ni/W-ZrO 2 coating is reduced, inhibiting the effect of dispersion enhancement. In addition, the denseness of the coating is affected due to the excess ZrO 2 affecting the deposition reaction of the matrix Ni/W on the aluminum alloy substrate surface, resulting in less hardness [20].…”

Influencing Factor Analysis of Interception Probability and Classification-Regression Neural Network Based Estimation

“…The factors influencing the probability of interception are complex and coupled with each other, so it is impossible to use formulas for accurate calculations, and the Monte Carlo method requires considerable simulation time. A method for quickly estimating the probability of interception is needed, to provide a basic reference for missile parameters design, to make rapid decisions in combat, or to optimize the interception configuration on the premise of ensuring defense requirements [39,40].…”

Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

“…In the paper [4], a novel hybrid ANN was presented to optimize the grit-blasting process to improve thermal spraying coatings' structural properties and corrosion-resistance performance. A similar application of ANNs was included in [5]; i.e., a backward propagation neural network was applied to efficiently optimize the pulse electrodeposition process of Ni-W-graded coating. Another example of using ANNs in materials science was described in the paper [6].…”