Modeling of PEO Coatings by Coupling an Artificial Neural Network and Taguchi Design of Experiment

• Published in: Journal of materials engineering and performance 2024-07, Vol.33 (14), p.7111-7122
• Main Authors: Shahri, Z., Allahkaram, S. R., Soltani, R., Jafari, H.
• Format: Article
• Language: English
• Subjects: Characterization and Evaluation of Materials; Chemistry and Materials Science; Corrosion and Coatings; Engineering Design; Materials Science; Original Research Article; Quality Control; Reliability; Safety and Risk; Tribology
• ISSN: 1059-9495; 1544-1024
• DOI: 10.1007/s11665-023-08459-3

An artificial neural network (ANN) was developed to predict the corrosion resistance of MgO coatings produced by the plasma electrolyte oxidation process on ZX504 alloy, with experimental data from the Taguchi method for training and performance evaluations. Process variables, i.e., chemical composition, current density, frequency, and duty cycle, were considered as inputs; and the corrosion resistance (measured by electrochemical impedance spectroscopy technique) as output data. According to the simulation results in the training stage, a high correlation coefficient was achieved between predicted and measured values (~ 0.99). Using this well-trained ANN model, the proposed ANN model could predict corrosion resistance with a mean square error of approximately 0.0085 made from the test dataset. Examination of the surface morphology suggested a correlation between the microstructure and corrosion performance. The results showed that samples coated at lower Na 3 PO 4 , frequency, duty cycle, and higher KF and current density have the highest corrosion resistance.