Experimental and Artificial Neural Network Modeling of the Effects of the Input Parameters on Tool Wear and Surface Roughness in Vibration Assisted Turning of Ti6Al4V

In ultrasonic vibration-assisted turning, an ultrasonic vibration is added to the tool, which leads to the periodical disengagement of the tool and the work-piece. In this research, an experimental study of ultrasonic vibration-assisted turning and conventional turning on Ti6Al4V Titanium alloy is conducted. First, by analyzing different parameters, four parameters are selected as the main affecting input parameters (cutting speed, feed rate, depth of cut, and ultrasonic vibration), and the effects of these four parameters are studied on two output parameters, namely tool wear and surface roughness. After the experimental tests, a statistical analysis is performed on the results and a neural network model is developed to predict the tool wear and surface roughness. The results show that the developed neural network model has a good agreement with the experimental results. In all experiments using ultrasonic vibrations, the tool wear and surface roughness were lower in comparison with the conventional turning. The cause of the tool wear and surface roughness reduction in ultrasonic mode are reducing the average forces applied to the tool, the alternative disengagement between the tool and the workpiece and increased dynamic stability of the process.