grinding parameter optimization of ultrasound-aided electrolytic in process dressing for finishing nanocomposite ceramics

grinding parameter optimization of ultrasound-aided electrolytic in process dressing for finishing nanocomposite ceramics

;Fan Chen;Bo Zhao;Xiao-feng Jia;Chong-yang Zhao;Jing-lin Tong
journal of power sources 2016 Vol. 2016 pp. -
135
chen2016mathematicalgrinding

Abstract

In order to achieve the precision and efficient processing of nanocomposite ceramics, the ultrasound-aided electrolytic in process dressing method was proposed. But how to realize grinding parameter optimization, that is, the maximum processing efficiency, on the premise of the assurance of best workpiece quality is a problem that needs to be solved urgently. Firstly, this research investigated the influence of grinding parameters on material removal rate and critical ductile depth, and their mathematic models based on the existing models were developed to simulate the material removal process. Then, on the basis of parameter sensitivity analysis based on partial derivative, the sensitivity models of material removal rates on grinding parameter were established and computed quantitatively by MATLAB, and the key grinding parameter for optimal grinding process was found. Finally, the theoretical analyses were verified by experiments: the material removal rate increases with the increase of grinding parameters, including grinding depth (ap), axial feeding speed (fa), workpiece speed (Vw), and wheel speed (Vs); the parameter sensitivity of material removal rate was in a descending order as ap>fa>Vw>Vs; the most sensitive parameter (ap) was optimized and it was found that the better machining result has been obtained when ap was about 3.73 μm.

Citation

ID: 253561
Ref Key: chen2016mathematicalgrinding
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
253561
Unique Identifier:
10.1155/2016/7896035
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet