Optimization and sensitivity analysis of geometrical and process parameters to reduce vibration during end milling process

Selection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude.