Study on the Cutting Performance and Remaining Life Prediction of Micro-Texture Ball End Milling Cutters for Titanium Alloys

Abstract

As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. This heat leads to chip adhesion and exacerbates the wear of the ball end milling cutter, ultimately affecting its service life. Therefore, studying the residual life of the tool during the cutting process is essential to prevent significant impacts on the product’s surface quality due to tool damage and passivation. Most research on micro-texture cutters is based on experiments that analyze the wear patterns of cutters under various lubrication conditions and their influence on the cutting process. Different neural network prediction models are employed to enhance the accuracy and stability of tool life prediction models. However, the exploration of other superior models for predicting the life of micro-texture cutters remains ongoing. This paper is based on an experiment involving the milling of titanium alloy using a micro-pit-structured ball end milling cutter. It was found that the cutting force of the tool is higher during the initial and later wear stages. During the stable wear stage, the unevenness of the defective layer on the tool surface is reduced, increasing the contact area and reducing the surface pressure, thereby decreasing the cutting force. This study analyzes the influence of micro-pit structural parameters on the wear and milling force of the ball end milling cutter. By evaluating the wear value of the ball end milling cutter after each cut, the wear mechanism of the micro-texture cutter is identified. A deep-learning-based bidirectional long short-term memory (BiLSTM) neural network model for tool life prediction is developed. Through training and validation, the model’s accuracy and stability are continuously improved. A comparative analysis with different predictive models is conducted to determine whether the proposed model offers advantages over existing models, which is crucial for maximizing tool utilization and reducing manufacturing costs.