Research on CT intelligent detection system for mining drill pipe

With the increase of coal mining depth and the complexity of geological conditions, automatic drilling machines have been widely used, and underground rapid drilling robots have put forward higher requirements for the quality and safety of drill rods. The traditional drilling rod detection method requires manual completion of the upper and lower drilling rods, and is highly subjective, time-consuming, and labor-intensive, making it difficult to meet the needs of modern coal mine production. This article integrates the Computed Tomography (CT) detection intelligent control system into the industrial production field of coal mine drill pipes to achieve intelligent manufacturing integration design. The equipment mainly includes radiation and detection systems, electrical control systems, computer control, and safety protection systems. Mechanical drilling rods are used for loading and unloading, and CT imaging is used to inspect the welding quality of joints. Practice has proven that it can shorten the manual loading and unloading time. The error rate between CT scanning detection results and actual measurement results is less than 1.32<3.68, which meets production requirements and ensures the efficiency of drill pipe detection. It provides new ideas for improving the efficiency and accuracy of drill pipe detection, and guarantees the efficiency of rapid drilling robot drilling.

Authors

Institutions

Publication Details

Published
2025-09-18
DOI
https://doi.org/10.1117/12.3077980
Primary Topic
Geoscience and Mining Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on CT intelligent detection system for mining drill pipe

Yi Qin, Yuqing Xiao
Geoscience and Mining Technology
article

Research on CT intelligent detection system for mining drill pipe

Yi Qin, Yuqing Xiao
article en

Abstract

With the increase of coal mining depth and the complexity of geological conditions, automatic drilling machines have been widely used, and underground rapid drilling robots have put forward higher requirements for the quality and safety of drill rods. The traditional drilling rod detection method requires manual completion of the upper and lower drilling rods, and is highly subjective, time-consuming, and labor-intensive, making it difficult to meet the needs of modern coal mine production. This article integrates the Computed Tomography (CT) detection intelligent control system into the industrial production field of coal mine drill pipes to achieve intelligent manufacturing integration design. The equipment mainly includes radiation and detection systems, electrical control systems, computer control, and safety protection systems. Mechanical drilling rods are used for loading and unloading, and CT imaging is used to inspect the welding quality of joints. Practice has proven that it can shorten the manual loading and unloading time. The error rate between CT scanning detection results and actual measurement results is less than 1.32<3.68, which meets production requirements and ensures the efficiency of drill pipe detection. It provides new ideas for improving the efficiency and accuracy of drill pipe detection, and guarantees the efficiency of rapid drilling robot drilling.

Institute of Disaster Prevention (CN), Chongqing 2D Materials Institute (China) (CN)
Openalex Percentile: Top 69%
Geoscience and Mining Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on CT intelligent detection system for mining drill pipe — Yi Qin, Yuqing Xiao · (2025) | TGRS Research Map | TGRS