The Application of Artificial Intelligence Technology in the Selective Laster Sintering

Article Preview

Abstract:

Based on laser sintering constituency as the research object, this paper aimed at the perspective of artificial intelligence technology. It uses the new control theory and research method of BP neural network algorithm and tries to provide reference for optimizing the sintering process of laser district. This paper argues that the application of artificial intelligence technology to laser sintering constituency. Through the simulation, it can make up for the inadequacy of the traditional control method. Under certain conditions, the goal of process optimization will be achieved by finding the optimal parameters.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

236-239

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Zhang Nixu , Wenshang, Wang Wenwen. Coal mine machinery30(2009), pp.3-6, (In Chinese).

Google Scholar

[2] Liu Weijun, rapid prototyping technology and its application: Mechanical industry press, 2004, pp.142-145. (In Chinese).

Google Scholar

[3] Yuan Hongling : Rapid prototyping/rapid molding integrated manufacturing system of precision error: Hefei university of technology, (2005).

Google Scholar

[4] Wen XianShi, Yusheng, Huang Shuhuai, Mechanical science and technology, 23(2004), pp.235-237. (In Chinese).

Google Scholar

[5] Wang Peng , Wang Xiangwei, Zheng Dayu, Mechanical engineer, 8(2008), pp.39-40. (In Chinese).

Google Scholar

[6] Dong Qu: Based on the simulation of selective laser sintering process optimization research: Suzhou university, 2010, pp.18-28, (In Chinese).

Google Scholar

[7] Long Gong Ming. Metallurgical industry press, 18(2010), p, 26-39. (In Chinese).

Google Scholar

[8] Yuan Hongling, YangYing, Cui Guoming. Engineering plastics applications, 17(2007), pp.67-87. (In Chinese).

Google Scholar

[9] C. Chungchoo, D. Saini. On-line tool wear estimation in CNC turning operations using fuzzy neural network model. International Journal of Machine Tools & Manufacture. 2002, 42: 29-40.

DOI: 10.1016/s0890-6955(01)00096-7

Google Scholar