Predictive Modeling of Surface Integrity and Material Removal Rate in Computer Numerical Control Machining: Effects...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Mechanical Engineering

Background:

• Computer numerical control (CNC) machining performance is influenced by material properties.
• Key performance indicators include material removal rate (MRR), surface roughness (Ra), and surface waviness (Wa).
• Understanding the interplay between material characteristics and machining outcomes is vital for industrial applications.

Purpose of the Study:

• To investigate the impact of thermal conductivity and hardness on CNC turning performance.
• To analyze the effects on MRR, Ra, and Wa across various engineering materials.
• To develop a predictive model for MRR based on material properties and machining parameters.

Main Methods:

• Experimental investigation of five engineering materials (Aluminum 6061, Brass C26000, Bronze C51000, Carbon Steel 1020, Stainless Steel 304).
• Measurement of MRR, Ra, and Wa during CNC turning operations.
• Development and validation of a multivariable regression model incorporating cutting speed, feed rate, thermal conductivity, and hardness.

Main Results:

• Materials with high thermal conductivity (>100 W/m·K) showed up to 38% higher MRR and better surface integrity.
• Aluminum 6061 exhibited the highest MRR and best surface finish, while Stainless Steel 304 showed the lowest MRR and poorest surface quality.
• The regression model achieved high predictive accuracy (R² > 0.92) for high-conductivity materials, with deviations in harder, low-conductivity materials.

Conclusions:

• Thermal conductivity and hardness are critical factors in CNC turning performance.
• Material-specific optimization of machining parameters is essential for enhancing productivity, surface quality, and tool longevity.
• Findings are applicable to high-precision industries like aerospace, automotive, and biomedical manufacturing.