Research of EDM Titanium Alloy TC11 Based on GM(1,N) Model and BP Neural Network

Ji Gao; Yao Sun; Di Wang

doi:10.4028/www.scientific.net/AMM.556-562.395

Paper Titles

Experimental Research on Impacts of Operation Parameters on Agglomeration Characteristics during CFB Biomass Gasification
p.375

First Principle Calculation of CO Chemisorption on δ-Pu(001) Surface
p.380

Crystallization Characteristic Study for Amorphous of Nd60Al10Cu20Ni10
p.385

Tensile and Impact Behavior and Fracture Characteristics of FGH96 Powder Metallurgy Superalloy
p.389

Research of EDM Titanium Alloy TC11 Based on GM(1,N) Model and BP Neural Network
p.395

Research on Alkali-Activator and its Effects on Mechanical Properties of Slag-Based Geopolymer at early Age
p.399

Research on Continuous Cooling Transformation Curve of a C-Si-Mn Steel
p.404

Synthesis of a Novel D-A Type Photoelectrical Material: 5,5'-(9,9-Dioctyl-9H-Fluorene-2,7-Diyl)bis(Thiophene-2-Carbaldehyde)
p.408

Study of the High-Pressure HCP Phase of Al: A Computer Aided Design and Simulation
p.412

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Research of EDM Titanium Alloy TC11 Based on...

Research of EDM Titanium Alloy TC11 Based on GM(1,N) Model and BP Neural Network

Abstract:

In order to predict the influences of electrical discharge machining (EDM) and enhance material removal rate (MRR). A model was presented for predictions of MRR in EDM TC11 test piece based on the construction method of gray GM(1,N) model and BP neural network model (GNNM).The results show that the GNNM is credible with the maximum absolute error 3.58%, the minimum absolute error 0.01% and the mean error 0.53%were obtained .The results indicated that the model can reflect the technological law of EDM TC11 and successfully predict its processing speed .The paper provides references and basis for selecting processing parameters of EDM TC11 and has practical significance.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

395-398

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.395

Citation:

Cite this paper

Online since:

May 2014

Authors:

Ji Gao*, Yao Sun, Di Wang

Keywords:

BP Neural Network (BPNN), EDM, GNNM, MRR, TC11

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Hsiao YF, Tarng YS, Huang WJ (2008) Optimization of plasma arc welding parameters by using the Taguchi method with the gray relational analysis. Mater Manuf Process 23(1): 51–58.

DOI: 10.1080/10426910701524527

Google Scholar

[2] Kim HR, Lee KY (2008) Using the orthogonal array with gray relational analysis to optimize the laser hybrid welding of a 6061-T6 Al alloy sheet. Proc IME B J Eng Manufacture 222(8): 981–987.

DOI: 10.1243/09544054jem1070

Google Scholar

[3] Acherjee B, Kuar AS, Mitra S Misra D (2011) Application of gray-based Taguchi method for simultaneous optimization of multiple quality in laser transmission welding process of thermoplastics. Int J Adv Manuf Technol56: 995-1006.

DOI: 10.1007/s00170-011-3224-7

Google Scholar

[4] Hsiao YF, Tarng YS, HuangWJ (2008) Optimization of plasma arc welding parameters by using the Taguchi method with the gray relational analysis. Mater Manuf Process 23(1): 51–58.

DOI: 10.1080/10426910701524527

Google Scholar

[5] Liu B, Zhu HY, Zhang K, et al. The equipment and process of NC high efficiency beehive grinding machining. Electro-machining &Mold. J 2005; 1: 45-47.

Google Scholar

[6] E. Uhlmann,S. Piltz and D. Oberschmidt. Machining of micro rotational parts by wire EDG . Production Engineering. J 2008; 2(3): 227-233.

DOI: 10.1007/s11740-008-0094-4

Google Scholar

[7] Nagai M, Yamaguchi D Grey theory and engineering application method. Tokyo(2004).

Google Scholar

[8] Zhu DQ, Shi H . Principle and Application of Artificial Neural Networks. Beijing: Science Press, 2006., P . 108.

Google Scholar