Multi-objective robust evolutionary optimization of the boring process of AISI 4130 steel

Boring is widely applied to enlarge holes. The high L/D ratio of boring bars enables self-excited vibration, deteriorating the quality of the hole. Therefore, this work aims the multi-objective evolutionary robust optimization of the boring process. Robust parameter design is employed to achieve robust models for roughness and roundness concerning tool overhang length and borehole depth, set as noise variables. These models aid the attainment of control factors’ levels, i.e., feed, cutting speed, and fixture position, which turn the responses less sensitive to noise. The robust models together with the material removal rate deterministic model are optimized through evolutionary multi-objective methods. The effects of process and noise factors are discussed considering literature. The multi-objective evolutionary optimization of the robust models helps to achieve these robust levels of process factors besides balancing the trade-off between the outcomes. The multi-objective robust evolutionary results outperform the scalarization approach considered for comparison purposes.