Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel

In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.