Adaptive Method to Reduce Thermal Deformation of Ball Screws Based on Carbon Fiber Reinforced Plastics.

Clicks: 358
ID: 54038
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #85 of 812 articles by views in Materials (Basel, Switzerland)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 812 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
A In high-speed precision machining, thermal deformation caused by temperature rise affects the accuracy stability of the machine tool to a significant extent. In order to reduce the thermal deformation of ball screws and improve the accuracy, a new adaptive method based on carbon fiber reinforced plastics (CFRP) was proposed in this study and the thermal deformation of ball screws was determined. By using the sequential coupling method, the thermal-structural coupling analysis of a ball screw was conducted based on the finite element method (FEM). The analysis results were verified through a comparison with the experimental results. Based on the verification, an FE model of the improved ball screw was established to study its thermal characteristics. The key design parameters of the improved ball screw were optimized based on the Kriging model and genetic algorithm (GA). The thermal reduction effect of the improved ball screw was validated through the experimental results. The results indicate that the adaptive method proposed in this research is effective in reducing the thermal deformation of ball screws.
Reference Key
gao2019adaptivematerials Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gao, Xiangsheng;Qin, Zeyun;Guo, Yueyang;Wang, Min;Zan, Tao;
Journal Materials (Basel, Switzerland)
Year 2019
DOI
E3113
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.