The Study of EDM TC11 Surface Discharge Mark Diameter Based on the Artificial Neural Network Modeling

Ji Gao; Di Wang; Yao Sun; Shuai Wang

doi:10.4028/www.scientific.net/AMM.490-491.586

Paper Titles

An Inspection Period Analysis of the Grinder Rods Eccentric Wear of the Rod-Pumped well Based on the Multi-Linear Regression Equation
p.564

The Numerical Simulation on Diesel Particulate Filter Based on CFD Technology
p.569

Use of Drawings Archive for Design Process
p.573

Structure Design of a Machining Center Bed Based on Topology Optimization Technique
p.580

The Study of EDM TC11 Surface Discharge Mark Diameter Based on the Artificial Neural Network Modeling
p.586

Modal Analysis of Diamond Wire Cutting Machine's Bed
p.590

Numerical Simulation for Residual Stress and Deformation of Surface Welding on Membrane Water-Wall
p.594

Study on Feed Offset in Elliptical Vibration Cutting
p.600

The Springback Predication for a Pillar Reinforcement Plate with Drawing Surface Optimization
p.607

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 490-491The Study of EDM TC11 Surface Discharge Mark...

The Study of EDM TC11 Surface Discharge Mark Diameter Based on the Artificial Neural Network Modeling

Abstract:

The process parameters of electrical discharge machining, such as : workpiece polarity, pulse width, pulse interval, peak current, peak voltage, all have influence on TC11’s surface roughness.But general methods are difficult to determine the relationship between the process parameters and the process indicators. This article established a artificial neural network model of EDM TC11 surface discharge mark diameter which can forecast. Neural network algorithm used BP algorithm, the network structure was the 2-4-1.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 490-491)

Pages:

586-589

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.490-491.586

Citation:

Cite this paper

Online since:

January 2014

Authors:

Ji Gao, Di Wang, Yao Sun, Shuai Wang

Keywords:

Artificial Neural Network Model, Electrical Discharge Machining (EDM), Surface Discharge Mark Diameter

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yongqing Zhao, Zhengping Xi. The Current Research on Aviation Titanium Alloy Materials in China. Journal of Aeronautical Materials , 2003 (10) ( In Chinese).

Google Scholar

[2] Some investigations into the electric discharge machining of hardened tool steel using different electrode materials Shankar Singh, S. Maheshwari , P.C. Pandey Journal of Materials Processing Technology 149 (2004) 272–277.

DOI: 10.1016/j.jmatprotec.2003.11.046

Google Scholar

[3] Rong Wu, Shian Liu, Yaojie Tian. Based on Neurosolutions Artificial Neural Network Expert System for Electrical Discharge Machining Process. Non-traditional Machining , 2008 (12) (In Chinese).

Google Scholar

[4] Haoquan Yang, Ping Zhou. Based on Artificial Neural Network Modeling Method of Electrical Discharge Machining Process . Mechanical Design and Manufacturing, 2010 (1) (In Chinese).

Google Scholar

[5] Hongrui Yan, Liju Ma. Based on The Fuzzy Neural Network Intelligent Prediction Model Design and Realization of The Modern , Electronic Technology , 2008 (2) (In Chinese).

Google Scholar