Optimization of CNC Turning Parameters for Surface Roughness in Brass Using Response Surface Methodology (RSM) - M Mazlan, Nur Izaaqila; Ibrahim, Badaruddin (Research and Innovation in Technical and Vocational Education and Training, 2025)
The manufacturing industry, which plays an important part in world economies, is always looking for ways to improve efficiency and precision, especially in machining operations. High precision and surface quality can now only be attained with CNC lathes, which are essential for machining rotating workpieces. The cutting speed, feed rate, and depth of cut optimization during the roughing process of Brass C3604, an alloy known for its strength and machinability, is the subject of this study. Finding the best set of these parameters is the main goal to reduce surface roughness and improve machining quality. The response surface methodology (RSM) and analysis of variance (ANOVA) are employed to design and analysis trials, taking a technical approach. Surface roughness will be measured using a stylus profilometer and a cutting tool coated with carbide (CNMG120408). The purpose of this research is to benefit the manufacturing sector by offering insights into parameter optimization, which will enhance the efficiency and quality of Brass C3604 machining. Recommended cutting parameters for this study include cutting speeds of 40 - 70 m/min, feed rates of 0.08 – 0.13 mm/rev, and depths of cut ranging from 0.5 mm. The numerical optimization of parameters for surface roughness reveals the optimal combination of a cutting speed of 70 m/min and a feed rate of 0.080 mm/rev, resulting in a surface roughness of 1.675 µm and the highest desirability score of 0.602. However, the ANOVA hypothesis indicates that the linear model is significant, with an F-value of 5.30 and a p-value of 0.0343, suggesting that the model explains a significant portion of the variability in the response variable. Future studies should explore a broader range of cutting speeds, feed rates, and depths of cutting to understand how these factors interact under more varied conditions.