Experimental investigation and optimization of manufacturing processes of Ni–P–Y2O3 composite coatings by multiple linear regression method based on genetic algorithm

• Published in: International journal of advanced manufacturing technology Vol. 126; no. 9-10; pp. 3995 - 4019
• Main Authors: Abdesselam, Yassine, Rezgui, Imane, Naoun, Mahiedine, Belloufi, Abderrahim, Mezoudj, Mourad, Zerrouki, Djamal
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2023; Springer Nature B.V
• Subjects: CAE) and Design; Cathodes; Coatings; Computer-Aided Engineering (CAD; Current efficiency; Efficiency; Electrodeposition; Engineering; Genetic algorithms; Industrial and Production Engineering; Manufacturing; Mathematical models; Mechanical Engineering; Mechanical properties; Media Management; Microhardness; Optimization; Original Article; Parameter identification; Process parameters; Regression analysis; Yttrium oxide; Ni–P–Y; Genetic algorithm; nano-composite coating; Electrodeposition; Cathode current efficiency; Microhardness; Optimization; O
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-11342-z

The nature and complexity of the manufacturing process for composite coatings make it difficult to predict or even measure the technical and economic performance of manufacturing, which is why great attention has been paid to the manufacturing methodologies of these coatings and their properties, in particular the mechanical properties. In this work, an experimental approach for the manufacture of Ni–P–Y
2
O
3
composite coatings by cathode process from sulfate-based electrodeposition baths was presented. This approach can improve the mechanical properties of the coating and the rate of deposition exhibited by the cathode current efficiency. A new technique for identifying processing parameters using a multi-input–output system based on the technique of multiple linear regression has been proposed. The objective was to determine the influence of the electrodeposition parameters on the quality of the coatings developed in order to conduct an optimization based on the genetic algorithm of these parameters that ensures the obtaining of a high micro-hardness coating, with the maximum possible efficiency of the electrodeposition cell. It is shown that the elaborated model is able to give results providing a very good correlation between the real and predicted values, and the experimental results were found to be close to the predicted values within 1.95% error range for cathode current efficiency and 1.58% error range for micro-hardness. The values used for the validation of the elaborated models differ from the values used for the construction of the fuzzy rules. The new optimization strategy used in this study made it possible to determine the optimal values that allow obtaining a coating with high micro-hardness (592 Hv) compared to other coatings of the same family with a high efficiency of electrodeposition cell.