UBC Theses and Dissertations

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UBC Theses and Dissertations

Virtual prediction of volumetric errors of 5-axis machine tools Asgari Pirbalouti, Mohammad Javad


Numerically controlled 5-axis machine tools are highly susceptible to potential errors due to their complex geometry. The machine tools comprise prismatic and revolute joints, and errors in each joint's geometry can cause a deviation from the desired tool position and orientation, resulting in volumetric errors. These errors can significantly affect the machine's performance, precision, and operation cost. Therefore, accurately predicting volumetric errors is crucial in developing compensation strategies. This thesis presents a generalized volumetric error model for all classes of machine tools, including rotary table, spindle rotary, and hybrid types. A screw-theory-based kinematic model is employed, which can be generalized to map the axis commands into the workpiece coordinate system, defining the relative motion of the tooltip with respect to the workpiece. The error model incorporates forty one geometric errors, and the effect of different classes of machines is studied to identify the position-independent geometric errors of rotary axes. A laser interferometer is used to measure fifteen position-dependent geometric errors of linear axes, including linear positioning, straightness, and angular errors. A Ballbar system is used to measure the remaining errors in a specific rotational test involving both linear and rotational axes to record the machine’s performance in a circular motion, including the position-dependent and independent geometric errors of the rotary axes and squareness errors. As the Ballbar recorded measurements are influenced by more than one error parameter, a decoupling method is implemented to individually identify each error parameter of rotary axes. The effectiveness of the proposed method is validated by machining a Pyramid-shaped test part under chatter-free conditions. The machined part is measured using a coordinate measuring machine, and the results demonstrate that the prediction method is 90% accurate for three-axis machining and 75% for five-axis machining.

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Attribution-NonCommercial-NoDerivatives 4.0 International