# Blasting Fragmentation Study Using 3D Image Analysis of a Hard Rock Mine
## 2 Materials and Methods
### PortaMetrics\({}^{\text{TM}}\) Image Analysis  
After capturing the images, an artificial intelligence algorithm named Fragmentation Artificial Intelligence (FMAI) performed the processing and thus provided a precise image of the captured region of interest (ROI). The data obtained were sent to the MetricsManager(tm) Pro platform, displaying the same data in the cloud, allowing the fragmentation results to be shared remotely. In this platform, the user could view each image with date, time, and location information because the captured images were associated with the specific location of the blasting site with an integrated global positioning system (GPS) [19].  
In the MetricsManager(tm) Pro platform, there were resources for manual correction to help the automatic analysis function and improve the results. It was possible to adjust the images, such as adjusting the ROI, manually joining or dividing rock fragments, delimiting the fines region, and delineating a fragment that was not automatically detected. Editing tools were used to improve the detection of fragment boundaries. The processes were repeated for other images in the same blast stack. The algorithm used to determine the fragment size considered the longest dimension of the rock and its shape factor [18]. Finally, with the measurement of the sizes of the blocks and particles, the distribution curve of the percentage pass and the table of cumulative sizes were generated for each image.  
The images were chosen to create reports that included certain information, such as the average parameter in which 80% of the material passed through the sieve the particle size distribution curve, the statistical frequency of size, the location, and the slope measurements of the face of the bench [19].  
In order for the PortaMetrics analysis to occur in a justified manner, its activity sequencing must have been previously detailed with maximum precision, to the point of validating how the equipment performs in the face of an investigation. To this end, the sequencing of the activities governed by it is based primarily on capturing the fragmentations with a tablet with 3 cameras available and the possibility of investigating the 3D analysis. After that, the model can be calibrated with the operator handling the equipment, or even in the software portal, where the fragmentation images are inserted via the digital cloud. With regard to these parameters, being delimited and sequenced according to the desired granulometry, some requirements must be respected, such as avoiding the presence of shadows and respecting the granulometry and distance limits, which are considered to be 32.81 feet in distance and a minimum granulometry of 1.18 inches. At the end, the fragmentation curve obtained manually via two models, the Swebrec and the Kuz-Ram model, becomes available, with the latter being selected for comparisons in this work in the equipment PortaMetrics.  
A well-known and used technique in comparison to PortaMetrics is WipFrag, whereby, as seen in Figure 3, it appears that despite the precision presented by the device, it needs the support of an object for delimitation and the use of a measurement parameter, which is not needed in PortaMetrics [21].  
So that the visualization of the operationalization was mostly evidenced, operational sequencing of the post-blasting PortaMetrics was carried out according to Figure 3. It is worth noting that the entire operation was recorded right after the primary dismantling, with Figure 3 being highlighted as a flowchart, thus leaving the operating mode of the equipment as perceptible.