Optimization of Process Parameters for electrodeposited Watts Nickel Coating Using a Genetic Algorithm Approach

We utilized a Back Propagation (BP) neural network combined with a Genetic Algorithm (GA) to optimize the parameters of the Watts nickel plating process, aiming to enhance the coating hardness. The BP neural network model used current density, pH value, boric acid concentration, and bath temperature as input parameters, while coating hardness was the output. The introduction of quadratic polynomial features allowed the model to achieve a coefficient of determination (R2) of 0.973. After GA optimization, the optimal parameter combination was identified as: current density of 1.3 A dm–2, pH value of 3.6, boric acid concentration of 20.5 g·L–1, and plating bath temperature of 42.3℃. The results reveal that orientation and grain size significantly affect the microhardness of nickel coatings. Specifically, the (220) crystal plane most strongly influences hardness, followed by the (111) crystal plane, while the (200) crystal plane has the least effect. By optimizing the electroplating process parameters, it is possible to control the crystal orientation of the coating, thereby affecting the hardness of the coatings. There is a negative correlation between grain size and the hardness. Additionally, In Watts nickel plating, the current density, pH value, boric acid concentration, and bath temperature jointly affect the mechanical properties of the coatings, their synergistic action determines the coating quality. We demonstrate that combining GA with neural networks is an effective method for optimizing the deposition parameters and solution chemistry of Watts nickel plating and improving the quality of the coatings.