# Influence of Cutting-Edge Micro-Geometry on Material Separation and Minimum Cutting Thickness in the Turning of 304 Stainless Steel

## TL;DR

This paper studies how cutting-edge micro-geometry affects material separation and surface quality when machining 304 stainless steel.

## Contribution

A predictive model for minimum cutting thickness and microstructure evolution is developed using analytical modeling, simulation, and experiments.

## Key findings

- Minimum cutting thickness increases with tool nose radius, with a stable ratio of 0.25 to 0.30.
- Asymmetric edge morphology causes minimum cutting thickness to first increase and then decrease with K-factor variation.
- A dislocation density-based subroutine accurately predicts surface layer microstructure properties like grain size and hardness.

## Abstract

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking “cutting edge micro-geometry—material separation behavior (separation point/minimum uncut chip thickness)—microstructural evolution of the machined surface.” This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel.