A Research For Precision Spindle Radial Rotation Error Separation Technology Based On The Three-Point Method

Machine tool spindle is the core component in modern machining, the machining precision of workpiece depends on spindle accuracy. So the study of the spindle is particularly important. The direct expression of the spindle space vibration are roundness error and rotation error, through researching above two kinds of error, the motion of a machine tool spindle can be clearly grasped. This paper chooses the three-point error separation method as it theoretical basis to study and surrounds improving separation precision of roundness error and rotation error.To begin with, this paper comparatively analyzes the current common error separation methods and finally chooses the three-point error separation method which has a certain research value to study. Then, the basic principle of the three-point error separation method is stated, through analyzing, it is found that the weight function plays an important role in this technique. When weight function chosen inappropriately, the phenomenon of harmonic suppression will occur,which produces incorrect separation results.Next, from the perspective of mathematical analysis, the characteristics of weight function are studied. Around weight function, two standards put forward for improving the accuracy of error separation—overall importance and locality. On the basis of the above two standards, non-differentiable optimization objective function is made into a continuously differentiable optimization function by the maximum entropy function. Combined with particle swarm optimization algorithm, the optimized global sensor's installation angle got can prevent harmonic suppression phenomenon. Simulation results show that the global optimal value greatly improves the error separation precision.Finally, in view of the effect from sensor noise on error separation effects, this paper further puts forward a set of sensor noise signal optimization method. This method no longer taking the research of weight function but the weight coefficient and weight function of the three-point error separation method as a unified whole, and establishes a new optimization objective function. In combination with particle swarm optimization algorithm, the optimal value is obtained under noise suppression. Theoretical analysis and simulation results show that the angle can still make error separation precision maintain a high level, and the angles of sensor have a good inhibition effect on noise signal. Considering the deviation of sensor installation angle may lead to low error separation accuracy and harmonic suppression phenomenon, this paper presents a method to overcome this shortcoming, the interval optimization. Through the use of the method, the decline due to installation deviation of error separation accuracy is effectively solved.To sum up, this paper aims at improving the separation accuracy around the three-point method. According to different problems, global optimal value, optimal value under noise suppression and the interval optimal value for sensor's installation angle are obtained. In the end, through simulation analysis and experimental testing, the reliability of the theoretical research and the correctness of the optimization results are fully demonstrated.