Optimization of CNC Machining Parameters Using Machine Learning Techniques

Abstract

Computer Numerical Control (CNC) machining plays a vital role in modern manufacturing industries by enabling high-precision and efficient production of complex components. However, achieving optimal machining performance remains challenging due to the complex interactions between process parameters such as cutting speed, feed rate, depth of cut, and tool geometry. Improper selection of these parameters can lead to poor surface finish, increased tool wear, and reduced productivity. This study focuses on the optimization of CNC machining parameters using machine learning techniques to enhance machining efficiency and output quality. The methodology involves the collection of experimental machining data under varying cutting conditions, followed by preprocessing steps including normalization, feature selection, and outlier removal. Machine learning models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest algorithms are employed to model the relationship between input parameters and performance responses such as surface roughness, tool wear rate, and material removal rate. Optimization techniques, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are integrated with machine learning models to identify optimal machining conditions. The performance of the proposed approach is evaluated using metrics such as prediction accuracy, mean absolute error, and optimization efficiency. The results indicate that machine learning-based optimization significantly improves machining performance compared to conventional trial-and-error and statistical methods. ANN and ensemble models demonstrate high predictive accuracy, while hybrid optimization approaches effectively identify optimal parameter combinations.