Articles | Volume 8, issue 2
https://doi.org/10.5194/ms-8-277-2017
https://doi.org/10.5194/ms-8-277-2017
Research article
 | 
01 Sep 2017
Research article |  | 01 Sep 2017

A dynamic thermal-mechanical model of the spindle-bearing system

Haitong Wang, Yonglin Cai, and Heng Wang

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.

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Short summary
A dynamic thermal-mechanical model of the spindle-bearing system is proposed to investigate the transient thermal characteristics. A spindle-bearing system with adjustable and measurable preload is designed and constructed. Validated by the experiments, thermal feedbacks on boundary conditions and thermal contact conductance are non-ignorable for the accurate simulation. The proposed model could also be generalized in other mechanical systems to improve the prediction accuracy.