# Development of Automatic Electric Drive Drilling System for Core Drilling
## 6 Conclusions and Discussion  
In this study, the 2-DOF dynamics of the core drilling are modeled in detail, where the connections between drill bit and power head are treated as rotational springs. Then, A purely electric drive electronic control system using PLC technology and a servo motor servo system was used to develop a diamond-impregnated core drilling system that can be used to realize automatic drilling. Using the dynamic model of drill string and linearization control technology, a control system was developed to realize the process control of the drilling process. Then, four drilling modes were developed for the system: the performance of the different modes was verified experimentally, and the constant-speed mode was recommended as the conventional drilling mode. Finally, A power optimization drilling experiment based on motor performance was conducted, and a reasonable power optimization drilling procedure is provided herein. In the experiment, a \(\varphi\)46 diamond-impregnated drill bit was used to drill the rock samples, and a vast amount of data were obtained by changing the rotary speed and ROP. The power optimization results show that the optimal rotary speed was in the range of 700-1000 rpm, and the optimal ROP was in the range of 2.0-2.6 m/h. Compared with the previous laboratory automatic drill system, this study redesigned and developed the automatic electric drive drilling system with higher WOB and rotation speed for the characteristic of core drilling. Overall, this study provides a practical solution for electrically driven core drilling system in the laboratory, from complete setup to control design and validation.  
However, the drilling system was in the early stages of development; only concrete was used instead of rock samples for drilling, although we used the same bit diameter as in the actual project. Moreover, this system has not been adequately tested on hard rocks. In addition, we used only a simple optimization algorithm to achieve optimization with high time complexity. We provide interfaces for further optimization; after optimization, we aim to compute the fast responses in real time to achieve intelligent drilling. Another problem is that because of the shallow drilling depth, the change in pump pressure is not noticeable; therefore, the system feedback cannot be completed according to the change in pump pressure, which is what we need to optimize. In future, experiments will be conducted to validate the drilling performance of the system using different rock types, and a machine learning algorithm will be applied to attain optimization while drilling and achieve formation identification.  
Figure 15: Chart of energy consumption per footage at different ROPs.  
**Author Contributions:** Conceptualization, Z.Z., Y.H. and B.L.; methodology, Z.Z., Y.H. and B.L.; software, Z.Z. and K.D.; validation, Z.Z., Y.Z. and K.D.; investigation, Z.Z., Y.Z. and K.D.; resources, Z.Z., Y.Z. and K.D.; data curation, Z.Z., Y.Z. and K.D.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z. and Y.H.; supervision, Y.H. and B.L.; project administration, Y.H.; funding acquisition, Y.H. and B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China (2016 YFE0202200).  
**Data Availability Statement:** The dataset of the experiment can be found from [[https://dx.doi.org/10.21227/vqkd-7f86](https://dx.doi.org/10.21227/vqkd-7f86)]([https://dx.doi.org/10.21227/vqkd-7f86](https://dx.doi.org/10.21227/vqkd-7f86)) accessed on 1 August 2022.  
**Acknowledgments:** The authors would like to thank greatly [PERSON] at the China University of Geoscience (Beijing) for their helpful discussions and comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.