Development of a new model for the fixture design and clamping optimization

  • Enhua Cao
  • , Jianhua Su
  • , Zhiyong Liu
  • , Hong Qiao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Workpiece deformation must be controlled in the manufacturing process and other engineering application. Fixture configuration (position), clamping force and temperature are main aspects that influence the degree and distribution of Workpiece deformation. This paper takes large optical glass as an example, develop a new multiple kernel learning method to discuss the optimal fixture design. The proposed method uses two layers regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Since that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers. Then, based on the proposed model, the optimal magnitude and positions of clamping forces can be obtained. The experiments show is effective for the optical element clamping optimization analysis.

Original languageEnglish
Title of host publicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages359-364
Number of pages6
EditionMarch
ISBN (Electronic)9781479958252
DOIs
StatePublished - 2 Mar 2015
Externally publishedYes
Event2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
NumberMarch
Volume2015-March

Conference

Conference2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/TerritoryChina
CityShenyang
Period29/06/144/07/14

Keywords

  • Integrated fixturing model
  • Multiple kernel regression
  • Optimal fixture design

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