A sustainable approach-based optimization of internal diamond burnishing operation

Abstract

The related work of the diamond burnishing processes focused on improvements in surface quality. The study aims to optimize burnishing factors, including the spraying distance of the nozzle (S), the inlet pressure of the cold air (I), and the quantity of the liquid CO₂ (L) of the cool and cryogenic-assisted diamond burnishing operation for minimizing energy consumed (EC) and arithmetical mean surface height roughness (Sa). Burnishing responses are modelled based on the radial basis function network and full factorial data. The entropy method, improved grey wolf optimizer, non-dominated sorting genetic algorithm II, and technique for order of preference by similarity to the ideal solution were implemented to calculate the weights, produce solutions, and select the best outcome. As a result, the optimal data of the S, I, and L were 15.0 mm, 3.0 bar, and 11.0 L/min, respectively. The Sa and EC were reduced by 20.4% and 3.8%, respectively, at the optimality. The optimized outcomes could be employed to improve energy efficiency and machining quality for the internal diamond burnishing process. The optimizing technique could be used to solve complicated issues for different burnishing operations. The cool and cryogenic-assisted diamond burnishing process could be utilized for machining different internal holes.