Machine-learning-based optimization of hybrid electrochemical magnetorheological finishing process to achieve nano finishing on additively manufactured biomaterial

Abstract

Powder Bed Fusion-Laser Beam (PBF-LB) is a form of additive manufacturing that entails the incremental layering of materials to construct complex multi-layered structures. The precise comprehension of the temporal and spatial variations of the entire structure. Individual tracks, layers, and the molten pool are indispensable for regulating aberrant deposition patterns and fabricating targeted PBF-LB components. However, the PBF-LB fabricated parts’ poor surface quality is a significant challenge. The Hybrid Electrochemical Magnetorheological (H-ECMR) polishing technique integrates mechanical abrasion with electrochemical reactions to enhance the surface characteristics of parts created through additive manufacturing. Herein, the Magnetorheological (MR) is used as the polishing media, and its carrier medium is replaced with an electrolyte to enable an electrochemical reaction. In the present work, machine learning-based optimisation, i.e. Artificial Neural Network, is implemented to optimise the process parameters to attain maximum surface reduction. The average surface roughness (R<inf>a</inf>) value of 12.56 µm is lowered to 34.56 µm on the Ti-6Al-4 V polished surface at optimised process parameters. Furthermore, the electrochemical reaction between the workpiece and the electrolyte forms a dense and consistent oxide layer on the polished surface, increasing the corrosion resistance of the PBF-LB fabricated part. © 2025 Informa UK Limited, trading as Taylor & Francis Group.