Model-based cutting load prediction and feed rate optimization considering cutting conditions and tool wear

Abstract

The feed rate is one of the key factors in determining cutting load during machining processes. Cutting load varies depending on the materials of the tool and workpiece, cutting conditions, and tool wear, all of which significantly impact machining performance and quality. Due to these reasons, both pre-optimization and adaptive control methods have been studied to optimize feed rates. This study focuses on developing and validating a cutting load prediction model and a feed rate optimization model that account for the effects of tool wear in milling processes. The cutting load prediction model is based on orthogonal cutting geometry, allowing for real-time control and accurate prediction of cutting load variations due to tool wear. The feed rate optimization model dynamically adjusts the feed rate to maintain consistent cutting load, regardless of tool condition, improving machining efficiency and stability. Experimental results showed that the cutting load prediction model achieved an average accuracy of over 85%, and the feed rate optimization model successfully maintained consistent cutting load under various machining conditions. These models provide a robust framework for real-time machining optimization, significantly enhancing process stability, productivity, and quality. Moreover, by integrating the effects of tool wear, the models offer comprehensive solutions for industries requiring high precision and extended tool life, such as aerospace and automotive manufacturing.