Thermal error modeling with dirty and small training sample for the motorized spindle of a precision boring machine
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Data samples (temperature and thermal drifts) obtained in motorized spindle thermal experiments are usually small and contain much information. Some of the information cannot be fully comprehended by most regression modeling methods when modeling with small training samples; hence, the modeled thermal error predictors can seriously lack robustness, especially for the thermal tilt angle (vital for the boring accuracy of precision boring machines) predictors. To solve this problem, the LS-MLR (least-squares multivariable linear regression), the GA-SVR (genetic algorithm-support vector machine for regression), and the RFR (random forest regression) regression modeling methods are applied to construct the thermal error predictors (models) with the training sample, and the predictors are then evaluated with the testing sample. Comparisons of the three modeling methods are carried out afterwards; it is pointed out that predicting ability of the bias-corrected RFR predictor for thermal elongation is close to the GA-SVR predictor, and as for the thermal pitch and the thermal yaw, predicting ability of the RFR predictors are superior to the LS-MLR and the GA-SVR predictors. Finally, the evaluation results also indicate that the proposed RFR thermal error modeling method is promising to be used in motorized spindle thermal error predicting in machining processes even when the modeling data are poor.
KeywordsMotorized spindle Random forest regression Thermal elongation Thermal tilt angles Thermal error modeling
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The authors declare that they have no conflict of interest.
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