Position prediction and error compensation for a large thin-walled box-shaped workpiece in a fixture

The final machining quality will be adversely affected when the workpiece position held in a fixture is not consistent with the expected position. Predicting and compensating for workpiece position errors can improve machining accuracy. However, most predicting methods neglect the effects of locating surface microtopography, surface profile error, and deviation of applied clamping force on workpiece position error, which will easily lead to a poor prediction result of the low-rigidity workpiece position. Therefore, a comprehensive position prediction model is developed to predict the position error of a large thin-walled box-shaped workpiece in the type of fixture with one locating surface and two pins, in which the above three error sources are considered. In this paper, the fractal geometry theory is used to identify the microtopography of the contact surface, and the equivalent stiffness between the fixture locating plane and the workpiece datum plane is estimated to get the relationship between the clamping force and the inclined angle of the workpiece caused by contact deformation. According to the obtained relationship, an estimation model based on the measured data of some key sampling points on the workpiece is presented to calculate the deviation between the machining surface and its nominal envelope surface. Finally, the proposed position prediction model is validated by the finite element simulation. The verification results show that the proposed method is effective and practicable, which can be used to predict and compensate for the machining position error of the low-rigidity workpiece.

Automated summary

The study established a comprehensive position prediction model that can effectively predict and compensate for machining position errors of low-rigidity workpieces, improving machining accuracy.