Abstract

Abstract. This paper presents a dynamic thermal-mechanical model to investigate the thermal characteristics in a spindle-bearing system. In this model, transient thermal analysis, static structure analysis and calculation of the boundary conditions are conducted as a solution loop. The transient boundary conditions, such as bearing stiffness, bearing heat generation and thermal contact conductance, are calculated with the appropriate formulas and solution methodology. The thermal feedbacks, which are seldom considered in the previous studies, are calculated in details. In order to validate the prediction accuracy, thermal equilibrium experiment is conducted on a test rig of spindle-bearing system. The predictions of the proposed model, such as bearing preload, temperature and thermal displacements, are in close agreement with the experiment results. The comparisons of the proposed model with two traditional simulations show that, the thermal feedbacks on the boundary conditions and thermal contact conductance are of great importance to the real-time estimation of the thermal characteristics. The proposed model provides a practical method to improve the prediction accuracy. It could also be generalized in other mechanical systems to investigate the dynamic thermal characteristics.

Highlights

  • The development of the modern machine tools proceeds towards high precision and speed, and highlights the importance of thermal effects on mechanical systems

  • This paper presents a dynamic thermal-mechanical model of the spindle-bearing system

  • Based on the loop calling of the softwares of ANSYS and MATLAB, the proposed model provides an effective way to simulate the interactions between the boundary conditions and the Finite element model (FEM) analysis

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Summary

Introduction

The development of the modern machine tools proceeds towards high precision and speed, and highlights the importance of thermal effects on mechanical systems. There are generally two schools in the thermal-mechanical analysis of the spindle-bearing system. One school analyzes the thermal effects with the statistical models, such as regression (Yang, 2003; Wu and Kung, 2006) and neural network (Mize and Ziegert, 2000; Guo et al, 2010). Based on fitting analysis of the experiment results, the model reflects nonlinear relationship of thermal effects. Owing to the high efficiency of calculation, the statistical model is usually used in the thermal error compensation. The models could not be used to identify the thermal characteristics without the experiment results

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