# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 6 Conclusions  
In this study, we presented a novel approach for estimating the particle size distribution of blasted rock fragments (muckpile) by leveraging simulation and deep learning techniques applied to 3D point clouds. Our experiments demonstrated the effectiveness of employing simulation in the Cyber world to generate training data with known particle size distributions for deep learning, addressing the challenge of obtaining large quantities of real-world training data, which is often scarce and time-consuming to collect.  
By designing a particle size distribution estimation network that combines a Local Module, focusing on the local shape of rocks, and a Global Module, concentrating on the overall shape, we achieved a learning process that takes into account the local shape of rocks. In our experiments, we performed particle size distribution estimation by inputting 3D point cloud data reconstructed from actual muckpile into the pretrained estimation model developed in the Cyber world.  
The estimation results revealed that our approach could estimate particle size distributions that were generally close to the actual values. The stated accuracy of 96.3% and a loss function value of 0.1237 indeed pertains to the model's performance during the training phase. In practical estimation scenarios with real-world data, the accuracy might vary. Moreover, in models estimating different particle size distributions, the peaks of the eight particle size categories aligned, allowing us to obtain a distribution with a similar overall shape. These findings indicate that the particle size distribution estimation network developed in this study can effectively estimate the particle size distribution for actual muckpile, demonstrating its potential for practical applications in mining operations.  
In conclusion, our research showcases the potential of combining simulation and deep learning techniques for the estimation of particle size distributions in blasted rock fragments using 3D point cloud data. This approach paves the way for further advancements in the field, enabling more efficient and accurate analysis of muckpile in mining operations, ultimately contributing to improved safety, efficiency, and environmental impact management.  
Conceptualization, H.I., H.J. and T.A.; methodology, H.I., T.S. and K.Y.; software, H.I. and H.T.; validation, H.I., T.S. and K.Y.; formal analysis, H.I. and H.T.; investigation, H.I., T.S. and K.Y.; resources, H.I.; data curation, H.I.; writing--original draft preparation, H.I.; writing--review and editing, H.I.; visualization, H.I.; supervision, I.K. and Y.K.; project administration, I.K. and Y.K.; funding acquisition, H.J. and T.A. All authors have read and agreed to the published version of the manuscript.  
JSPS KAKENHI Fostering Joint International Research (B), grant number 21 KK0070.  
Not applicable.  
Data Availability Statement: Data are available upon reasonable request to the corresponding author.  
This work was supported by JSPS KAKENHI Fostering Joint International Research (B), grant number 21 KK0070.  
The authors declare no conflict of interest.