Prediction of 5-Axis Machining Characteristics of KP4 Material and Optimization of Tool Tilt Angle Using Deep Learning-Based CNN Machine Learning Model

In the production of products demanding high machining precision, such as precision molds, the application of 5-axis high-speed machining technology is essential to ensure superior cutting surface characteristics. In particular, optimizing the tool tilt angle—a critical variable determining the quality of the machined surface during 5-axis high-speed machining—is of utmost importance. However, quantitatively analyzing the effects of tool tilt angles on machining characteristics and establishing these as standardized process criteria have faced significant limitations. Therefore, this study conducted cutting experiments on KP4 material specimens using a 5-Axis High-Speed Machining center, systematically varying the tool tilt angle under standardized conditions, and assessed surface roughness parameters (Ra, Rz). Based on these experiments, a systematic analysis of the correlation between tool tilt angles and machining quality was performed to derive standardized process setting criteria for 5-axis high-speed machining technology. Additionally, an image dataset of cutting surfaces linked to machining characteristics based on tool tilt angles was established. Using this dataset, a deep-learning-based Convolutional Neural Network (CNN) machine learning model was developed. This model demonstrated high reliability in predicting machined surface quality, laying the foundation for standardizing and automating process parameter settings. This research provides practical guidelines for the standardization of 5-axis high-speed machining processes, thereby reducing quality deviations and ensuring consistent quality in industrial settings. In particular, the proposed standardized process conditions are expected to significantly contribute to establishing standard certification criteria and quality management systems in precision mold making, aerospace components, and high-precision machining industries.