A novel comprehensive framework for surface roughness prediction of integrated robotic belt grinding and burnishing of Inconel 718

Abstract

Surface roughness serves as a critical metric for assessing surface integrity, directly influencing the service performance of components. Hardening burnishing is a regularly used post-grinding procedure for Inconel 718 that can simultaneously reduce surface roughness and improve surface mechanical characteristics. A novel comprehensive framework is proposed to predict the surface roughness of Inconel 718 processed by the integrated robotic belt grinding and burnishing system developed in this study. Accounting for abrasive grain conditions and material removal rate during grinding, the L-BFGS-B optimization algorithm and image processing are utilized to predict the ground surface roughness first. Building upon this foundation, theoretical models are applied to ascertain the critical burnishing force needed to enter the hardening burnishing stage. Following this, the Expansion Cavity Model and Hertz theory are used to predict the roughness of the hardening burnished surface, which is only applicable when the step-over value exceeds a critical value. The proposed framework not only enhances our comprehension of the mechanisms underlying material surface deformation but also offers a novel avenue for predicting surface roughness during integrated robotic belt grinding and burnishing with high accuracy.