With the increasing problem of water pollution, oil–water
separation technology has attracted widespread attention worldwide.
In this study, we proposed laser electrochemical deposition hybrid
preparation of an oil–water separation mesh and introduced
a back-propagation (BP) neural network model to realize the regulation
of metal filter mesh. Among them, the coating coverage and electrochemical
deposition quality were improved by laser electrochemical deposition
composite processing. Based on the BP neural network model, the pore
size after electrochemical deposition could be obtained only by inputting
the processing parameters into the model, enabling the prediction
and control of the pore size of the processed stainless-steel mesh
(SSM), and the maximum residual difference between the predicted value
and the experimental value was 1.5%. According to the oil–water
separation theory and practical requirements, the corresponding electrochemical
deposition potential and electrochemical deposition time were determined
by the BP neural network model, which reduced the cost and time loss.
In addition, the prepared SSM was found to achieve efficient separation
of oil and water mixtures, reaching 99.9% separation efficiency in
a combination with oil–water separation, along with other performance
tests without chemical modification. The prepared SSM showed good
mechanical durability and the separation efficiency exceeded 95% after
sandpaper abrasion, thus, still maintaining the separation ability
of oil–water mixture. Compared to other similar preparation
methods, the method proposed in this study has the advantages of controllable
pore size, simplicity, convenience, environmental friendliness, and
durable wear resistance, offering important application potential
in the treatment of oily wastewater.