Abstract
Background: In precision machining, thermal error is the main source of machine tool error. And thermal error compensation is an effective method to reduce thermal error. Objective: In order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed. Method: This method is based on stepwise regression. According to the results of partial-F statistic, new variable is selected one by one, unapparent variables are deleted, and optimization selection of temperature measurement point is fulfilled, thermal error model of the NC machine tool is presented. Result: The new modeling method was used on NC machine tool, which reduced the temperature point number from 24 to 5. Moreover, model residual was less than 5µm after compensation. Conclusion: The result shows that the new thermal error model has higher prediction accuracy and less temperature variables.
Highlights
ObjectiveIn order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed
Thermal error compensation technique of NC machine tools is an important approach to improve the machining accuracy, and thermal error modeling is the basis of compensation
The result shows that the new thermal error model has higher prediction accuracy and less temperature variables
Summary
In order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed. According to the results of partial-F statistic, new variable is selected one by one, unapparent variables are deleted, and optimization selection of temperature measurement point is fulfilled, thermal error model of the NC machine tool is presented
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