Optimizing Machining Quality and Production Efficiency Through a Taguchi-Based Six Sigma Framework

This study introduces a Taguchi-based Six Sigma Optimization Framework (TSSOF) to enhance machining performance by optimizing key parameters in the CNC turning of EN8 steel.Focused on improving surface finish and material removal rate (MRR), the research applies Taguchi's Design of Experiments (DOE) and Six Sigma principles to analyze the effects of cutting speed, feed rate, and depth of cut.Using the signal-to-noise (S/N) ratio approach, optimal parameters were found to be a cutting speed of 125 m/min, feed rate of 0.1 mm/rev, and depth of cut of 0.3 mm, yielding the best surface finish.ANOVA reveals that feed rate is the most significant factor affecting surface roughness while cutting speed and depth of cut have lesser impacts.A regression model is developed to quantify the relationship between machining parameters and surface roughness, providing a data-driven optimization strategy.The results demonstrate that TSSOF effectively reduces defects, enhances efficiency, and improves process capability, making it valuable for automotive, aerospace, and advanced manufacturing industries.