Multi-response optimization of CNC 5-axis machine process parameters during micro end milling

What is it about?

This study focuses on optimizing the machining parameters during micro end milling of Titanium Grade 5 alloy using a 5-axis CNC machine. Titanium alloys are widely used in aerospace, biomedical, and automotive industries due to their high strength-to-weight ratio and corrosion resistance. However, machining titanium poses challenges such as tool wear, poor surface finish, and increased machining time. The research aims to achieve an optimal balance between surface roughness (Ra) and material removal rate (MRR) by employing the Taguchi L9 orthogonal array combined with Grey Relational Analysis (GRA) for multi-response optimization. Key process parameters—spindle speed, feed rate, and depth of cut—were varied systematically. The results reveal that feed rate is the most influential parameter, contributing 56.03% to overall performance, followed by depth of cut (27.39%) and spindle speed (13.96%). The optimal machining conditions were determined as spindle speed of 9000 rpm, feed rate of 150 mm/min, and depth of cut of 70 µm, which minimized surface roughness while maximizing MRR. This work provides practical recommendations for improving machining efficiency, reducing costs, and enhancing surface quality in precision manufacturing of titanium components.

Featured Image

Photo by Jonathan Castañeda on Unsplash

Photo by Jonathan Castañeda on Unsplash

Why is it important?

This research holds significant importance in the field of advanced manufacturing and precision machining, especially for industries working with titanium alloys. Titanium Grade 5 is widely used in aerospace, biomedical, marine, and automotive applications because of its excellent strength-to-weight ratio and corrosion resistance. However, it is notoriously difficult to machine due to its low thermal conductivity and high strength, which often lead to tool wear, increased costs, and compromised surface quality. By applying Taguchi-based Grey Relational Analysis (GRA) to optimize multiple machining responses simultaneously, this study provides a systematic and cost-effective approach to determine the best combination of spindle speed, feed rate, and depth of cut. Achieving minimum surface roughness ensures better aerodynamic and functional performance of components, while maximum material removal rate improves productivity and reduces machining time. The findings help manufacturers achieve higher efficiency, better surface finish, and longer tool life, which are crucial for industries where precision and quality are non-negotiable. Furthermore, the research bridges the gap between academic experimentation and real-world industrial application, offering valuable insights for engineers, researchers, and production teams working with micro-milling and advanced CNC processes.

Perspectives