| Abstract Scope |
Laser-assisted machining (LAM) effectively addresses challenges in machining Niobium C103, an advanced refractory alloy known for hardness, ductility, and adhesion issues. This study explores LAM parameters—laser power, cutting speed, feed rate, and laser-tool distance—and their impact on surface integrity, tool wear, and machining forces. Artificial Intelligence (AI) techniques, including response surface methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA), were utilized for modeling and optimization. ANN provided superior predictions, while GA efficiently identified optimal machining conditions, significantly enhancing machining efficiency and component quality, offering practical insights for industrial applications. |