Authors
Keywords
Abstract
This study provides an in-depth study advanced machining processes and their optimization using various machine learning algorithms. The study focuses on key machining parameters such as cutting speed (m/min), feed rate (mm/rev), and cutting depth (mm), and rotation speed (RPM), investigating their effects on surface roughness (Ra) in manufacturing operations. This research addresses emerging challenges in modern manufacturing, particularly in the processing of advanced engineering materials for the aerospace, automotive, and precision industries.These algorithms were selected for their ability to manage complex and non-linear relationshipsin manufacturing data and for their proven performance in predictive modelling. The study explores how these methods can overcome traditional limitations in process planning and optimization, especially in situations where conventional empirical models are inadequate.Special attention is paid to the theoretical foundations of each algorithm, in which linear regression serves as a basic model, random forest regression provides improved predictive capabilities through ensemble learning, and support vector regression provides robust optimization through its ε-insensitive loss function approach. The research also explores the important relationship between machine parameter optimization and surface quality, emphasizing the importance of parameter optimization in achieving desired surface properties while maintaining production efficiency.This study advances the field by providing a structured methodology machine parameter optimization, particularly relevant to computer-aided process planning and advanced manufacturing processes. These findings have significant implications for industries requiring high-precision manufacturing, providing insights into How can machine learning methods be used effectively? optimize machining processes, reduce production costs, and improve surface quality in modern manufacturing operations.
