Material Classification by Drilling

Material Classification By Drilling Diana LaBelle, John Bares, Illah Nourbakhsh Pages 1-6 (2000 Proceedings of the 17th ISARC, Taipei, Taiwan, ISBN 9789570266986, ISSN 2413-5844) Abstract: Underground coal mining is one of the most dangerous occupations. Years of effort have been dedicated to researching methods of characterizing mine roof and floor for improving the mining environment. This research investigates using a neural network to classify rock strata based on the physical parameters of a roof bolting drill. This paper presents our methodology, as well as early results based on drilling experiments conducted in the laboratory using a custom poured concrete test block. We have classified, with a trained network, the five layers of the test block with less than 5% error. Keywords: mining automation, rock classification, neural networks, drilling, coal interface detection DOI: https://doi.org/10.22260/ISARC2000/0088 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley