Neurofuzzy Implementation in Smart Toolpost To Improve Performance
Machining is a complex process that requires a high degree of precision with tight geometrical tolerance and surface finish. Those are confronted by the existence of vibration in the turning machine tool. Overcoming a micro level vibration of a cutting tool using smart materials can save old machines and enhance flexibility in designing new generations of machine tools. Using smart materials to resolve such problems represent one of the challenges in this area. In this work the transient solution for tool tip displacement, the pulse width modulation (PWM) technique is implemented for smart material activation to compensate for the radial disturbing cutting forces. A Neurofuzzy algorithm is developed to control the actuator voltage level to improve dynamic performance. The deployment of the finite element method in this work as a dynamic model is to investigate the ability of the in intelligent techniques in improving cutting tool accuracies. The influence of minimum number of PWM cycles with each disturbing force cycle is investigated in controlling the tool error growth. Toolpost structural force excitation due to the PWM cycles was not given adequate attention in previous publications. A methodology is developed to utilize toolpost static force-displacement diagram to obtain required activation voltage to shrink error under different dynamic operating conditions using neurofuzzy.
