Abstract
The deep core drilling has currently wide application—construction drills, geological exploration, drills for measuring pendulums in dams, etc. It is very important and useful to know the drilling process parameters precisely. The most important is the used torque, pressure and rotation speed of the drilling machine necessary for the optimal drilling process. These parameters depend on the strength of the drilled material, for example, rock, sand, clay, etc. and of course on the drilled diameter and applied pressure. In-time identification of the drilling parameters enables the optimization of the drilling process directly during the performance. Furthermore, it can serve for the real data acquisition, which could be used for later drilling planning in similar geological environment. Obtained data are also very useful for designing new drilling machines. They could be designed according to their future usage to the optimal performance. This contribution deals with the design of the new sensor for in-time identification of above-mentioned core drilling parameters. The sensor is based on the strain gauge measurement and enables online monitoring of the drilling process. The measured data are also stored and could be post-processed in longer time context to obtain also the drilling capacity of the drilled material.
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Reference
Dub M, Janda M, Mrázek J, Kavka M, Dynybyl V (2017) Core drilling machine for perfectly vertical drills. In: Herák D (ed) Proceedings of 58th International conference of machine design departments—ICMD 2017, pp 66–68. Czech University of Life Sciences Praha, Praha
Acknowledgements
This work was supported by The Technology Agency of the Czech Republic project No: TH02010958 “Sensor for measuring of operational parameters of deep core drilling”.
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Dub, M., Dynybyl, V. (2020). Sensor for In-time Identification of Deep Core Drilling Parameters. In: Medvecký, Š., Hrček, S., Kohár, R., Brumerčík, F., Konstantová, V. (eds) Current Methods of Construction Design. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-33146-7_28
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DOI: https://doi.org/10.1007/978-3-030-33146-7_28
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