Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28461
Title: Thermal Properties Prediction of Large-Scale Machine Tool in Vacuum Environment Based on the Parameter Identification of Fluid–Thermal Coupling Model
Authors: Li, T
Xi, G
Wang, H
Tang, W
Shao, Z
Sun, X
Keywords: vacuum environment;large-scale machine tool;flow-thermal coupling;parameter identification;temperature prediction
Issue Date: 18-Dec-2022
Publisher: MDPI
Citation: Li, T. et al. (2022) 'Thermal Properties Prediction of Large-Scale Machine Tool in Vacuum Environment Based on the Parameter Identification of Fluid–Thermal Coupling Model', Machines, 10 (12), 1237, pp. 1 - 26. doi: 10.3390/machines10121237.
Abstract: A high vacuum environment safeguards the performance of special processing technologies and high-precision parts such as nanosecond laser processing, chip packaging, and optical components. However, it poses higher requirements for the machine tool, which makes the temperature control of machine tools an important goal in design and development. In this paper, the thermal properties of a large-scale 5-axis laser processing machine tool in a vacuum environment were investigated. The thermal contact resistance between parts is identified by the parametric simulation and experiment. The whole machine temperature field was then obtained based on the fluid–thermal coupling model and verified by experiment. The results showed that the thermal contact resistance of the motor and reducer with the water cold plate was 560 W/(m2∙°C) and 510 W/(m2∙°C), respectively, and the maximum temperature increase of the machine was 3 °C. Based on the results, the machine tool’s temperature increase prediction chart was obtained by simulation under different processing conditions such as cooling water flow rate, cooling water temperature, motor speed, and ambient temperature. It provides technical and data references for the research on the thermal stability of the machine tool in processing.
Description: Data Availability Statement: Not applicable.
URI: https://bura.brunel.ac.uk/handle/2438/28461
DOI: https://doi.org/10.3390/machines10121237
Other Identifiers: ORCiD: Tianjian Li https://orcid.org/0000-0002-1888-7143
ORCiD: Han Wang https://orcid.org/0000-0002-1349-7226
1237
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).17.99 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons