DU Hongyang, TAO Tao, HOU Ruisheng, et al. First-order autoregressive modeling method for axial thermal error of machine tool spindle[J]. 2021, 53(7): 60-67.
DOI:
DU Hongyang, TAO Tao, HOU Ruisheng, et al. First-order autoregressive modeling method for axial thermal error of machine tool spindle[J]. 2021, 53(7): 60-67.DOI: 10.11918/202009022.
First-order autoregressive modeling method for axial thermal error of machine tool spindle
To solve the problem that the empirical modeling for thermal error of machine tool spindle lacks physical significance
and the modeling accuracy and robustness are greatly affected by pseudo-hysteresis effect of thermal deformation
a modeling method for thermal error of machine tool spindle which has clear physical meaning and is not affected by pseudo-hysteresis effect is derived from theoretical perspective. Firstly
the spindle is simplified as a one-dimensional rod. Taking into account the convection between cylinder surface and air
the analytical solutions of temperature field and thermal deformation of the rod under the condition of single-end heat source are obtained by using heat transfer theory. Then the thermal deformation expression is transformed
and the first-order autoregressive model for the rod under the condition of single-end fixed heat source is derived. After that
the validity of the first-order autoregressive model for the thermal deformation of the rod under the condition of variable heat source is verified
and the relationship between the coefficients of the autoregressive model and the physical characteristics of the spindle
the time interval of the autoregressive model and the heat source is pointed out. Finally
finite element simulation is carried out
and the experimental verification is performed on Headman T65 lathe. The simulation results show that the first-order autoregressive model can effectively estimate the thermal deformation of the one-dimensional rod under the condition of variable heat source and is not affected by the pseudo-hysteresis effect. The experimental results on the T65 lathe show that
compared with multiple linear regression model
the first-order autoregressive model is more robust and has clear physical significance.