Open AccessPREDICTION OF THERMAL GROWTH IN A HIGH-SPEED SPINDLE BY CONSIDERING THERMO-MECHANICAL BEHAVIOR
Author(s) -
Esra Yüksel,
Erhan Budak,
Emre Özlü,
Atacan Oral,
F. Igrek,
Fulya Tosun
Publication year - 2021
Publication title -
mm science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.195
H-Index - 10
eISSN - 1805-0476
pISSN - 1803-1269
DOI - 10.17973/mmsj.2021_7_2021055
Subject(s) - preload , bearing (navigation) , machining , materials science , transient (computer programming) , thermal , finite element method , machine tool , rotational speed , structural engineering , mechanical engineering , mechanics , engineering , computer science , metallurgy , thermodynamics , physics , medicine , hemodynamics , artificial intelligence , operating system
Continuous rotation of spindle bearings and motor cause thermally induced structural deformations and thermal growth, which is one of the main reasons for machining errors. A positive feedback loop between bearing preload and heat generation causes preload variations in spindle bearings. These preload variations demonstrate a nonlinear transient behavior until the gradual expansion of outer bearing rings after which the thermally induced preload variation behaves steadily. In this study, a Finite Element (FE) framework is presented for predicting steady preload variation on spindle bearings. The method involves a thermal loading model and a transient contact analysis. In the contact analysis phase bearing contact deformations (penetration and sliding) and pressure are predicted by considering contact algorithms in an FE software. A transient spindle simulation in FE is employed to predict the bearing temperature and thermal spindle growth by using the proposed method. The performance of the method is demonstrated on a spindle prototype through bearing temperature and thermal deformation measurements. Results show that the proposed method can be a useful tool for spindle design and improvements due to its promising results and speed without the need for tests.