Development of OCMNO algorithm applied to optimize surface quality when ultra-precise machining of SKD 61 coated Ni-P materials

In this paper, a new algorithm developing to solve optimization problems with many nonlinear factors in ultra-precision machining by magnetic liquid mixture. The presented algorithm is a collective global search inspired by artificial intelligence based on the coordination of nonlinear systems occurring in machining processes. Combining multiple nonlinear systems is established to coordinate various nonlinear objects based on simple physical techniques during machining. The ultimate aim is to create a robust optimization algorithm based on the optimization collaborative of multiple nonlinear systems (OCMNO) with the same flexibility and high convergence established in optimizing surface quality and material removal rate (MRR) when polishing the SKD61-coated Ni-P material. The benchmark functions analyzing and the established optimization polishing process SKD61-coated Ni-P material to show the effectiveness of the proposed OCMNO algorithm. Polishing experiments demonstrate the optimal technological parameters based on a new algorithm and rotary magnetic polishing method to give the best-machined surface quality. From the analysis and experiment results when polishing magnetic SKD 61 coated Ni-P materials in a rotating magnetic field when using a Magnetic Compound Fluid (MCF). The technological parameters according to the OCMNO algorithm for ultra-smooth surface quality with Ra = 1.137 nm without leaving any scratches on the after-polishing surface. The study aims to provide an excellent reference value in optimizing the surface polishing of difficult-to-machine materials, such as SKD 61 coated Ni-P material, materials in the mould industry, and magnetized materials.