Synergistic interaction effects of process parameters on surface finish in two-way AFM of SS446

The optimization of AFM process parameters for SS446 material is investigated through a comprehensive analysis integrating statistical methods and qualitative examination. Statistical analysis utilizing the Taguchi method, Grey Relational Analysis, Analysis of Variance, and regression modeling reveals critical insights into surface roughness improvement. Taguchi analysis identifies abrasive grain size as the most influential factor, with larger grain sizes significantly enhancing surface quality. Grey Relation Analysis emphasizes abrasive grain size's pivotal role in achieving optimal surface finish, elucidating its impact relative to extrusion pressure and number of cycles. ANOVA results confirm it along with moderate effects from extrusion pressure and number of cycles. Regression modeling provides a predictive framework for %ΔRa, offering practical insights into process optimization. Qualitative examination via Scanning Electron Microscopy images validates AFM's efficacy in rectifying surface defects and enhancing surface quality. These findings underscore the importance of abrasive grain size optimization in AFM processes and provide valuable guidance for industrial applications.