ePrints

Multiscale modelling and analysis for design and development of a high-precision aerostatic bearing slideway and its digital twin

Lookup NU author(s): Dr Dehong Huo

Downloads

• Published version [.pdf]

Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Aerostatic bearing slideways have been increasingly applied in the precision engineering industry and other high-tech sectors over the last two decades or so, due to their considerable advantages over mechanical slideways in terms of high motion accuracy, high speeds, low friction, and environment-friendly operations. However, new challenges in air bearings design and analysis have been occurring and often imposed along the journeys. An industrial-feasible approach for the design and development of aerostatic bearing slideways as standard engineering products is essential and much needed particularly for addressing their rapid demands in diverse precision engineering sectors, and better applications and services in a continuous sustainable manner. This paper presents the multiscale modelling and analysis-based approach for design and development of the aerostatic bearing slideways and its digital twin. The multiscale modelling and analysis and the associated simulation development can be the kernel of the digital twin, which cover the mechanical design, direct drive and control, dynamics tuning of the slideway, and their entire mechatronic system integration. Using this approach and implementation, the performance of an aerostatic bearing slideway can be predicted and assessed in the process. The implementation perspectives for the sideway digital twin are presented and discussed in steps. The digital simulations and digital twin system can be fundamentally important for continuously improving the design and development of aerostatic bearing slideways, and their applications and services in the context of industry 4.0 and beyond.

Publication metadata

Author(s): Gou N, Cheng K, Huo D

Publication type: Article

Publication status: Published

Journal: Machines

Year: 2021

Volume: 9

Issue: 5

Online publication date: 25/04/2021

Acceptance date: 23/04/2021

Date deposited: 03/06/2021

ISSN (electronic): 2075-1702

Publisher: MDPI AG

URL: https://doi.org/10.3390/machines9050085

DOI: 10.3390/machines9050085

Altmetrics

Share