Accurate modeling of material removal depth in convolutional process grinding for complex surfaces

Abstract

Soft tool grinding is promising as a flexible machining method known for its remarkable advantages, such as expansive contact areas, deformation-based compliance to curved surfaces, and effective vibration absorption, thereby ensuring heightened efficiency and superior surface quality. Nevertheless, attaining precise part contours necessitates an accurate model for material removal depth (MRD). Due to insufficient consideration of the softness of the tool and the intricate coupling of grinding parameters, existing methods fall short in clearly explaining the material removal mechanism and accurately calculating MRD. To address this gap, this paper first proposes an elastic extrusion model tailored for calculating the pressure distribution between soft tools and hard complex surfaces. Furthermore, complex surface grinding with soft tools is equivalent to the convolutional process grinding, revealing the nature of step-by-step propelling and layer-by-layer overlapping in material removal. Building on this insight, a dynamic local projection algorithm is presented for efficient and accurate MRD calculation. Simulations and experiments demonstrate significant improvements in the proposed methods compared to traditional methods, reducing errors by more than 49% for the contact model and more than 55% for the MRD model. The proposed methods not only lay new foundations for parameter tuning in soft tool grinding, but also contribute to promoting the understanding and application of precision grinding processes.