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The Research on Characterization of Crushability for Foundry Sand Particles

Dai Y., Ma Q. Y., Li X. H., Zhang X., Hu F. P., Zhang Y., Xie W. D.

Archives of Foundry Engineering

2017

Vol. 17, iss. 4

231--235

In this paper, crushability of foundry sand particles was studied. Three kinds of in-service silica sands in foundry enterprises selected as the study object, and foundry sand particles were subjected to mechanical load and thermal load during service were analyzed. A set of methods for simulating mechanical load and thermal load by milling and thermal-cold cycling were designed and researched, which were used to characterize the crushability for silica sand particles, the microstructure was observed by SEM. According to the user’s experience in actual application, the crushability of Sand C was the best and then Sand B, the last Sand A. The results indicated that mechanical load, thermal load and thermal-mechanical load can all be used to characterize the crushability of foundry sand particles. Microscopic appearances can qualitatively characterize the crushability of foundry sand particles to a certain extent, combining with the additions and cracks which are observed on the surface.

Remote Sensing Monitoring of Volcanic Ash Clouds Based on PCA Method

Li Ch., Dai Y., Zhao J., Zhou S., Yin J., Xue D.

Acta Geophysica

2015

Vol. 63, no. 2

432--450

Volcanic ash clouds threaten the aviation safety and cause global environmental effects. It is possible to effectively monitor the volcanic ash cloud with the aid of thermal infrared remote sensing technology. Principal component analysis (PCA) is able to remove the inter-band correlation and eliminate the data redundancy of remote sensing data. Taking the Eyjafjallajokull volcanic ash clouds formed on 15 and 19 April 2010 as an example, in this paper, the PCA method is used to monitor the volcanic ash cloud based on MODIS bands selection; the USGS standard spectral database and the volcanic absorbing aerosol index (AAI) are applied as contrasts to the monitoring result. The results indicate that: the PCA method is much simpler; its spectral matching rates reach 74.65 and 76.35%, respectively; and the PCA method has higher consistency with volcanic AAI distribution.