Artificial Neural Networks in Materials Science Application

Abstract:

Because but the artificial neural networks has the strong non-linear problem handling ability also the fault tolerance strong obtains the widespread application in the materials science.This article to its material design, the material preparation craft optimizes, the plastic processing, the heat treatment, the compound materials, corrode, domain and so on casting applications have carried on the discussion.

• Periodical: Applied Mechanics and Materials (Volumes 20-23)
• Pages: 1211-1216
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.20-23.1211
• Online since: January 2010
• Authors: Wen Yu Zhang
• Keywords: Application, Artificial Neural Network (ANN), Materials Science

References

[1] Lioushouji, Mawanzhen, Zhoupeixia etc, Artificial neural networks in materials science domain application, plastics industry, 2005，Vol. 33, No. 5, 162~164.

[2] Houzhezhe, Wujianjun, Artificial neural networks in material design application, Hebei industry university journal, 2003，Vol. 32, No. 3, 94~97.

[3] Fanxinmin, Kongjian, Jinbo, Artificial neural networks in materials science research application, materials guide journal, 2002，Vol. 16, No. 4, 28~30.

[4] Schooling J M, Brown M. Mater Sci Eng, 1999, A260: 222.

[5] Seed G M, Murphy GS. Fat Frat Eng Mater Struc, 1998, 21: 183.

[6] Gejianhua, Wangyingjun, zhengyudong, Artificial neural networks in materials science and processing application, modern chemical industry, 2003，Vol. 23, No. 1, 59~62.

[7] Dongmin, Artificial neural networks and material craft optimization, Liaoning normal training college university journal, 2000，Vol. 2, No. 3, 100~103.

[8] Liouxuefeng, Wanglingyun, Aluminum lithium alloy high temperature distortion changing stress artificial neural networks model, Chongqing university journal, 2001，Vol. 24, No. 2, 68~71.

[9] LI M Q, XIONG A M, HUANG W C, et al. Microstructural evolution and modelling of the hot compression of a TC6 Titanium alloy . Materials Characterization. 2003, 49: 203~209.

DOI: 10.1016/s1044-5803(02)00323-6

[10] Xuqiang, Zhangxinghong, Hanjiecai etc, Artificial neural networks in materials science application and forecast, Materials science and craft, 2005，Vol. 13, No. 4, 352~356.

[11] SONG R G, ZHANG Q Z. Heat treatment technique optimization for 7175 Aluminum alloy by an artificial neural network and a genetic algorithm. Journal of Materials PROCESSING TECHNOLOGY, 2001, 117: 84~88.

DOI: 10.1016/s0924-0136(01)01114-1

[12] Yangfanwen, Zhaoyueming, The artificial neural networks use in the high polymer material the performance prediction and the optimized design research progress, Computer and applied chemistry, 2001, Vol. 18, No. 2, 131~134.

[13] Valoor M T, Chandrashekhara K. A thick composite-beam model for delamination prediction by the use of neural networks. Composites Science and TECHNOLOGY, 2000, 60: 1773~1779.

DOI: 10.1016/s0266-3538(00)00063-4

[14] Guzhengbin, Lihejun, Likezhi, Artificial neural networks in compound materials research application, Journal of Astronautic Metrology and Measurement, 2003, Vol. 23, No. 4, 13~18.

[15] Maxiaoyan, Luanyanbing, Quzhuyu etc, Neural network in metal atmosphere rate of corrosion forecast application, Beijing science and technology university journal, 2001 ， Vol. 23, No. 2, 123~125.

[16] S Pintos, N V Queipo, O T D Rincon et al. Artificial neural network modeling of atmospheric corrosion in the MICAT project. Corrosion Sacience, 2000, Vol. 42, No. 1: 35.

DOI: 10.1016/s0010-938x(99)00054-2

[17] . P C Lu, M Urquidi-Macdonald. Prediction of IGSCC in Type 304 using an artificialneural networks[C]. Proc. Conf, Corrosion 94, Baltimore, MD, USA, 1994, nace, Paper151.

[18] M E Haque, K V Sudhakar. Prediction of corrosion-fatigue behavior of DP steel through artificial neural network. International Journal of Fatigue, 2001, Vol. 23, No. 1, 1.

DOI: 10.1016/s0142-1123(00)00074-8

[19] J Leifer, P E Zapp, J I Michalonis. Predictive models for determination of pitting corrosion versus inhibitor concentrations and temperature for radioactive sludage in carbon steel waste tanks. Corrosion, 1999, Vol. 55, No. 1, 31~35.

DOI: 10.5006/1.3283962

[20] Cuidawei, Linleyun, Zhaoyuehong, Applies the artificial neural networks to forecast the non-ferrous metal sea water corrodes long-term behavior, China corrodes and the protection journal, 2004，Vol. 24, No. 1, 29~32.

[21] Wanghaitao, Hanenhou, Kewei, In corrosion domain artificial neural networks application progress, Corrosion science and protection technology, 2004，Vol. 16, No. 3, 147~150.

[22] Lioujia, Jiashusheng, Neural network in casting production application, cast, 2004 ， Vol. 53, No. 1, 18~21. Author Brief Introduction: Zhang wen yu : woman, born in the February of 1968; senior engineer; now mainly engag in intelligence study work. Add: 023 letter box 5, luoyang henan; Postal code: 471039; TEL: [phone]; E-mail: ZWY68218@163. com.