The Mineral Classification with Sound Impacting by Artificial Neural Network
Main Article Content
Abstract
The highly skilled and experienced worker has been used to separate the raw corundum mineral from gangues since ancient times until the present day. This process has been studied to develop sound signals from free-falling corundum and other impurity minerals. At a height of 1 foot, impact sound signals in the time range of 200 ms were analyzed. It was found that frequencies from 500 to 5,000 Hz could not differentiate the waveforms. The study results revealed that the sound intensity level caused by the impact of mineral particles on a stainless steel bar was highest for corundum compared to other minerals. Iron ore falling through copper coils can cause electric field noise. Two input data were brought into the data training process by the Backpropagation Neural Network (BPNN), which learned to separate corundum from invaluable minerals. The results showed that the four groups of minerals can be separated by the sum of relationships between dependent and independent variables generated from BPNN using two variable weight parameters and a bias constant parameter. This successful research will be used to design hardware for mineral separation and other sensor applications related to mineral properties in the future.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The articles published are the opinion of the author only. The author is responsible for any legal consequences. That may arise from that article.
References
L. Wang, Y. Zeng, and T. Chen, “Back propagation neural network with adaptive differential evolution algorithm for time series forecasting,” Expert Systems with Applications, vol. 42, no. 2, pp. 855–863, 2015.
R. Abebe and M. Gopal, “Exploring the effects of vibration on surface roughness during CNC face milling on aluminum 6061-T6 using sound chatter,” Materials Today: Proceedings, vol. 90, pp. 43–49, 2023.
R.Hecht-Nielsen, “III.3-Theory of the Backpropagation Neural Network**Based on ‘nonindent’ by Robert Hecht-Nielsen, which appeared in Proceedings of the International Joint Conference on Neural Networks 1, 593– 611, June 1989. © 1989 IEEE.,” Neural Networks for Perception, pp. 65–93, 1992.
B. J. Wythoff, “Backpropagation neural networks: A tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 18, no. 2, pp. 115– 155, 1993.
D. Kim, “Normalization methods for input and output vectors in backpropagation neural networks,” International Journal of Computer Mathematics, vol. 71, no. 2, pp. 161–171, 1999.
R. S. Carmichael, Practical handbook of physical properties of rocks and minerals. Boca Raton, Fla.: Crc Press, 1989.
S. Siriluck, “Guideline for upgrading of a low grade coal by ‘NP & P Separation’ Technique,” The Journal of Industrial Technology, vol. 8, no. 3, pp. 84–92, 2014 (in Thai).
J. Guo, S. Y. O’Reilly, and W. L. Griffin, “Corundum from basaltic terrains: a mineral inclusion approach to the enigma,” Contributions to Mineralogy and Petrology, vol. 122, no. 4, pp. 368–386, 1996.
P. P. Sharma and S. C. Gupta, “Sand detachment by single raindrops of varying kinetic energy and momentum,” Soil Science Society of America Journal, vol. 53, no. 4, pp. 1005–1010, 1989.