Hybrid modelling for thermal deformation prediction of high speed motorized spindle

Fan, Lijun; Jing, Xishuang; Zhang, L. X.; Li, D.

doi:10.1088/1757-899x/399/1/012017

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…In recent years, many scholars have done a lot of research on thermal characteristics and thermal design of motorized spindle [6], which greatly improve the performance of motorized spindle. Fan et al [7] used partial least squares (PLS) to predict the thermal deformation of motorized spindle under different working conditions and analyzed the correlation between multiple temperature variables and three-dimensional thermal deformation, which improved the accuracy of the model. Uhlmanna et al [8] proposed a 3D finite element (FE) prediction model for high precision thermal characteristics of motorized spindle under complex boundary conditions such as heat source, convection and contact.…”

Section: Introductionmentioning

confidence: 99%

Co-simulation-based digital twin for thermal characteristics of motorized spindle

Fan

2023

Int J Adv Manuf Technol

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To improve the accuracy of thermal characteristics analysis of motorized spindle, an on-line correction model of thermal boundary conditions is proposed based on BP neural network (BPNN), the experimental data and simulation results are used to build the BPNN model to correct the thermal boundary conditions of motorized spindle. A digital twin system for thermal characteristics is developed based on the co-simulation of Ansys, Matlab, and LabVIEW to accurately predict the temperature field and thermal deformation of motorized spindle under different working conditions. The experimental results show that the prediction accuracy of temperature field accuracy of the motorized spindle is 98.62%, and the prediction accuracy of thermal deformation is 96.06%, which effectively improves the simulation accuracy of thermal characteristics, and provides the basis for the error compensation and thermal optimization design.

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Section: Introductionmentioning

confidence: 99%

Co-simulation-based digital twin for thermal characteristics of motorized spindle

Fan

2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

Hybrid modelling for thermal deformation prediction of high speed motorized spindle

Cited by 1 publication

References 15 publications

Co-simulation-based digital twin for thermal characteristics of motorized spindle

Co-simulation-based digital twin for thermal characteristics of motorized spindle

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