This analysis employs ANFIS and genetic algorithms to enhance surface roughness in titanium machining, indicating significant improvements in quality control.
Key Points
The ANFIS model achieved high prediction accuracy for surface roughness, suggesting reliable machining parameter adjustments.
Integration of genetic algorithms identified optimal conditions for dry turning, which notably reduced surface roughness.
Design of Experiments facilitated meaningful statistical analysis, emphasizing key machining parameters like spindle speed and feed rate.
The use of a digital twin framework supports smarter manufacturing processes, enhancing efficiency and quality in aerospace applications.