Multi-objective optimization of rotational magnetorheological abrasive flow finishing process
Abstract
The rotational magnetorheological abrasive flow finishing (R-MRAFF) technique achieves uniform, nano-level mirror finishes with high material removal (MR) rates, distinguishing it from other nano-finishing methods by combining rotational motion and magnetorheological (MaR) abrasive particles during the nano-finishing process. Regression analysis is conducted to assess the influence of input process parameters, namely, extrusion pressure (P), finishing cycles (N), rotational speed of the magnet (S), and abrasive mesh size (M) on the responses like percentage improvement in surface roughness (%ΔRa) and amount of material removed (MR). The obtained regression equations for %ΔRa and MR are then used to formulate a multi-objective optimization problem, which is solved by an elitist non-dominated sorting genetic algorithm-II (NSGA-II). The final results revealed a trade-off between these two objectives. The higher P, N, and S levels effectively generated a trade-off for the better surface finish (SF) and a good MR. However, the lower levels of M are adequate for both the responses. The study’s findings, particularly the identified optimal parameters’ combinations, offer valuable insights for maximizing the potential of R-MRAFF, enabling the attainment of desired SF and material removal characteristics in a range of applications. This study can be extended to other complex manufacturing processes with multiple parameters and responses.
Authors: Pushpendra Gupta, Vidyapati Kumar, Dilip Kumar Pratihar, Kalyanmoy Deb
Published in: CRC Press eBooks (2024)