0

Full Content is available to subscribers

Subscribe/Learn More  >

Thermal Error Modeling Based on Genetic Algorithm and BP Neural Network of High-Speed Spindle System

[+] Author Affiliations
Chi Ma, Liang Zhao, Hu Shi, Xuesong Mei, Jun Yang

Xi’an Jiaotong University, Xi’an, China

Paper No. IMECE2015-53030, pp. V02AT02A055; 12 pages
doi:10.1115/IMECE2015-53030
From:
  • ASME 2015 International Mechanical Engineering Congress and Exposition
  • Volume 2A: Advanced Manufacturing
  • Houston, Texas, USA, November 13–19, 2015
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5735-9
  • Copyright © 2015 by ASME

abstract

In order to improve the prediction accuracy of the thermal error models, grey cluster grouping and correlation analysis were proposed to optimize and select the heat-sensitive points to improve the performances of the thermal error model and minimize the independent variables to reduce modeling cost. Subsequently, the neural network with back propagation (BP) algorithm was proposed to construct the strongly nonlinear mapping relationship between spindle thermal errors and typical temperature variables. However, the shortcomings of the BP network restricted the accuracy, robustness and convergence of thermal error models. Then, a genetic algorithm (GA), which regarded the reciprocal of the absolute value sum of the differences between the predicted and desired outputs as the number of nodes in the hidden layer, was proposed to optimize the structure and initial values of the network. And the number of the nodes in the hidden layer can be determined by performing such operations of GAs. Moreover, the reciprocal of the sum square of the difference between the predicted and expected outputs of individuals is regarded as the fitness function and the weights and thresholds of the BP neural network are optimized by setting the control parameters of GAs. Then, the elongation and thermal tilt angle models of high-speed spindles were proposed based on BP and GA-BP networks and the fitting and prediction abilities were compared. The results showed that the grey cluster grouping and correlation analysis could depress the multicollinearity among temperature variables and improve the stability and accuracy of the thermal error models. Moreover, although the traditional BP network had better fitting ability, its convergence and generality were far worse than the GA-BP model and it is more suitable to use the GA-BP neural network as the thermal error modeling method in the compensation system.

Copyright © 2015 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In