A Neural Network-Based Application to Identify Cubic Structures in Multi Component Crystalline Materials Using X-Ray Diffraction Data.
One of the crystalline materials structures is cubic. An experimental study has been done about developing a scheme to identify the cubic structure types in single or multi component materials. This scheme is using fingerprints created from the calculation of quadratic Miller indices ratios and mat...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Dr. Sang H. Lee
2007
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/9385/1/A_Neural_Network-Based_Application_to_Indentify_Cubic_Structures_in_Multi_Component_Crystalline_Materials_Using_X-Ray_Diffraction_Data.pdf http://eprints.usm.my/9385/ http://ijcsns.org/index.htm |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | One of the crystalline materials structures is cubic. An
experimental study has been done about developing a scheme to identify the cubic structure types in single or multi component materials. This scheme is using fingerprints created from the calculation of quadratic Miller indices ratios and matches it with the ratio of the sin20 values from the diffracted data of material obtained by X-Ray Diffraction (XRD) method. These manual matching processes are complicated and sometimes are tedious because the diffracted data are complex and may have more than
one fingerprint inside. This paper proposes an application of multi-layered back-propagation neural network in matching the fingerprints with the diffracted data of crystalline material to quickly and correctly identify its cubic structure component types.
|
|---|