An Indicative Model Considering Part of the Thermo-Mechanical Behaviour of a Large Grinding Machine

Abstract

Machine tool (MT) thermal errors are an important element in ma-chined workpiece inaccuracies. In the past few decades, thermal errors associated mainly with one particular source (e.g. spindle or environment), have been successfully reduced by SW compensation techniques such as multiple linear re-gression analysis, finite element method, neural network, transfer function (TF) within similar calibration and verification conditions. An approach based on TFs is used for thermal error modelling in this research. This method respects basic heat transfer mechanisms in the MT and requires a minimum of additional gauges. The approach provides insight into the share of each source in the total machine thermal error through a combination of linear parametric models. The aim of this research is to develop an indicative model for a large grinding machine with predictive functionality focused on part of the thermo-mechanical behaviour within different configurations of the headstock, tailstock and workpiece. Unlike a compensation model, an indicative model has no connection to the MT feed drives and can only provide the machine operator with information regarding the actual direction and relative magnitude along with prediction of the time constant and steady state of the non-stationary thermal error. The second aim is to compare the difficulty of measuring at the stator and rotating machine part levels, the thermal behaviour linearity at both levels and the possibility of upgrading the indicative model to a compensation model to extend industrial applicability.

Thermal Characteristics of Spindle System Based on the Comprehensive Effect of Multiple Nonlinear Time-Varying Factors

“…For non-heating component in the spindle system, s(t) = 0 and for components that generate heat, s(t) is obtained according to Equation (8).…”

“…They measured the minimum deviation of the model through experiments as 7.6%. Marez et al [8] developed an approach based on TFs which is used for effective thermal error modeling for machine tools. This approach provides insight into the share of each source in the total machine thermal error through a combination of linear parametric models.…”