Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

Bayesian networks modeling for thermal error of numerical control machine tools

The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.

Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

Thermally induced spindle angular errors of a machine tool are important factors that affect the machining accuracy of parts.It is critical to develop models with good generalization abilities to control these angular thermal errors.However,the current studies mainly focus on the modeling of linear thermal errors,and an angular thermal error model applicable to different working conditions has rarely been investigated.Furthermore,the formation mechanism of the angular thermal error remains to be studied.In this study,an analytical modeling method was proposed by analyzing the formation and propagation chain of the spindle angular thermal errors of a five-axis computer numerical control(CNC)machine tool.The effects of the machine tool structure and position were considered in the modeling process.The angular thermal error equations were obtained by analyzing the spatial thermoelastic deformation states.An analytical model of the spindle angular thermal error was established based on the geometric relation between thermal deformations.The model parameters were identified using the trust region least squares method.The results showed that the proposed analytical model exhibited good generalization ability in predicting spindle pitch angular thermal errors under different working conditions with variable spindle rotational speeds,spindle positions,and environmental temperatures in different seasons.The average mean absolute error(MAE),root mean square error(RMSE)and R2 in twelve different experiments were 4.7μrad,5.6μrad and 0.95,respectively.This study provides an effective method for revealing the formation mechanism and controlling the spindle angular thermal errors of a CNC machine tool.