Temperature field model and control strategy in gravity casting process.

Clicks: 241
ID: 81823
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
Emerging

Ranked #21 of 39 articles by views in The Review of scientific instruments

Most read Least read

Bar heights use a square-root scale.

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
Sign in to mint
Abstract
Temperature control is one of the most important processes during aluminum (Al) alloy engine cylinder head product casting. An improper temperature control may result in no uniformity and microstructure defects in casting parts and give rise to high defect ratio. In this paper, a mathematical model with high nonlinearity, strong coupling, and less uncertainty is developed for the solidification process in Al alloy casting. The interfacial heat transfer coefficient is combined with the mold structure comprehensively to build the temperature-structure model, and the characteristics of the uncertainty conversion are also used in order to achieve optimal temperature control during the solidification process. The cloud model integrated with Proportion-Integral-Differential (PID) temperature control system enables evaluation of the uncertainty conversion quantitatively. By inputting the temperature error and the temperature error rate, the PID inference is output through the cloud inference engine to achieve the optimal temperature curve. The superiority of the control algorithm was verified on a customized experimental platform with the temperature control system. Compared with manual operation and traditional PID control, the result shows that the error of the cloud model control is lower than the manual operation and traditional PID control. The experimental results also suggest that the performance of our cloud model is better than that of the manual operation model and the traditional PID control model regarding to stability and controllability.
Reference Key
fan2019temperaturethe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fan, Qigao;Zhuang, Xiangpeng;Zhu, Yixing;Li, Yueyang;Xie, Linbo;Pan, Peng;Peng, Ran;
Journal The Review of scientific instruments
Year 2019
DOI
10.1063/1.5124111
URL
Keywords
baseball elbow imaging, magnetic resonance pediatric sports medicine

Citations

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

Comments

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