ISSN:
1662-8985
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Artificial neural network (ANN) is an intriguing data processing technique. Over the lastdecade, it was applied widely in the chemistry field, but there were few applications in the porousNiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosionexperiments were used to build a three-layer BP (back propagation) neural network model. Accordingto the registered BP model, the effect of process parameters including heating rate ( ), green density( ) and particle size of Ti ( d ) on compressive properties of reacted products including ultimatecompressive strength (vDσ ) and ultimate compressive strain (ε ) was analyzed. The predicted resultsagree with the actual data within reasonable experimental error, which shows that the BP model is apractically very useful tool in the properties analysis and process parameters design of the porousNiTi SMA prepared by thermal explosion method
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/40/transtech_doi~10.4028%252Fwww.scientific.net%252FAMR.41-42.135.pdf
