Optimization of Cutting Parameters in CNC Machining for Enhanced Surface Finish and Tool Life

Abstract

Optimizing cutting parameters in CNC machining is pivotal for enhancing surface finish and extending tool life. This paper investigates the critical parameters—cutting speed, feed rate, depth of cut, tool geometry, and coolant use—and their impact on machining performance. By exploring empirical, analytical, and machine learning-based optimization techniques, the study provides a comprehensive overview of methods used to refine CNC machining processes. Empirical approaches include experimental design and Taguchi methods, while analytical techniques involve mathematical models and optimization algorithms. Machine learning approaches, such as predictive modeling and adaptive control systems, offer advanced solutions for real-time parameter adjustment. The paper also presents case studies from aerospace, automotive, and electronics industries, demonstrating successful optimization implementations and their benefits. Key challenges such as material variability and machine tool limitations are discussed, alongside emerging trends like advanced materials and smart manufacturing technologies. The findings underscore the significance of parameter optimization in improving machining efficiency, reducing production costs, and achieving high-quality results. Future research directions are proposed to address ongoing challenges and leverage new technologies for continued advancements in CNC machining.