Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems

Honing processes are usually employed to manufacture combustion engine cylinders and hydraulic cylinders. provides a crosshatch pattern that favors the oil flow. In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) models were obtained for tool wear, average roughness Ra, cylindricity material removal rate in finishing honing processes. addition, multi-objective optimization with desirability function method was applied, order determine process parameters allow minimizing roughness, error while maximizing rate. The results showed grain size tangential velocity should be at their minimum levels, density, pressure linear maximum levels. If only wear considered, then low size, recommended, density vary, depending on algorithm employed. This work will help select appropriate processes, when minimized.