A Study on the Impact of Feed Rate and Spindle Speed on Surface Roughness in Ball End Milling of Stavax Steel Using Artificial Intelligence

Look, Chun Chuen (2025) A Study on the Impact of Feed Rate and Spindle Speed on Surface Roughness in Ball End Milling of Stavax Steel Using Artificial Intelligence. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

This research aims to optimize the machining parameters for STAVAX steel using a ∅6mm carbide-coated ball end mill, focusing on minimizing surface roughness—a critical factor in industries like aerospace, automotive, and mould manufacturing where surface finish affects product performance and longevity. The study addresses the challenge manufacturers face in determining optimal feed rates and spindle speeds to achieve superior surface finishes. By conducting controlled experiments, data on surface roughness under various machining parameters will be collected. This data along with published sources and experimental data from previous works will train a Machine Learning (ML) model to develop a predictive tool that correlates feed rate and spindle speed with surface roughness outcomes. The ML model’s predictions will be validated against experimental results to ensure accuracy and reliability. The expected outcome is a comprehensive understanding of the way machining parameters affect surface roughness in STAVAX steel, providing manufacturers with a valuable tool to optimize processes, reduce reliance on trial-and-error methods, and enhance efficiency. The findings will contribute to the advancement of AI applications in precision manufacturing and serve as a foundation for future studies in the field.