Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3765 - 3765
Published: March 29, 2025
The optimization of the manufacturing conditions in milling processes composes a crucial task for enhancing machining efficiency and extending tool’s lifespan. This study presents an investigation cutting performance under varying parameters via generation experimental dataset that was obtained through laboratory-controlled operations. Based on this dataset, artificial intelligence (AI) models, including neural network (ANN), k-nearest neighbors (KNN), support vector regression (SVR), were developed order to predict life as function conditions. Additionally, finite element method (FEM) simulations conducted estimate tool wear analyze process at numerical level. In particular, FE models utilized compute forces corresponding stress fields, well assess based certain variables. Furthermore, comparative analysis between AI-driven forecasts FEM performed evaluate their effectiveness reliability. findings provide insights into advantages limitations both methodologies, guiding coated carbide performance. outcomes contribute advancement predictive modeling processes, offering data-driven approach improved assessment.
Language: Английский