Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining

Abstract

Hybrid structures of metals and composite materials are increasingly common in aerospace industry, and the optimization and monitoring of the machining of these stacks are an area of active research. Online tool condition monitoring in particular is a valuable capability and is facilitated by real-time treatment of cutting force signals. Cutting force signals are considered one of the most important measures for tool condition monitoring. The present work treats online cutting force time series with fractal analysis. The signal features generated are central to tool wear assessment. This work evaluates the fractal dimension of the cutting force signals from orbital drilling of a stack of carbon fiber–reinforced plastics (CFRP) and Ti6Al4V titanium alloy as a measure of the “roughness” of these signals. It is shown that distinct wear stages are adequately identified using fractal signal features. Low machining quality may thereby be prevented. Additionally, to address the inconvenient need for long machining tests when studying the application of these techniques to CFRP and titanium alloys, a novel fractal index is proposed to improve the monitoring process without requiring extensive experimentation.