The mutually coupled thermal error of spindle and feed shafts for a lathe strongly affects precision of workpieces. A coupled thermal error model is proposed and implemented for machine tools. For a Headman HTC550/500 precision lathe
the coupled thermal error of spindle and feed shafts is decoupled
and fuzzy clustering is used to optimize the temperature measuring points. Subsequently
a multi-variable linear regression model for coupled thermal error is established and applied. The results show that the coupled thermal error model coincides with the lathe's actual situation; the fuzzy clustering effectively lowers the multicollinearity among temperature variables to improve the prediction accuracy; the prediction accuracy in x and z directions reaches 88.4% and 90.7% for spindle
and 82.9% and 71.3% for feed shafts
so the accuracy of the lathe is improved by 60.3% in x direction and by 56.6% in z direction after compensation.
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references
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