Statistical estimation of blast fragmentation by applying stereophotogrammetry to block piles

Han, Jin-Suk; Song, Jae-Joon

doi:10.1016/j.ijrmms.2014.02.010

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…3D reconstruction methods based on stereo vision are simpler and require less expensive hardware setups [24]. The work in [25] has successfully applied a multiple view approach to separated samples, while the system in [26] has reconstructed 3D models of particles placed on a conveyor belt.…”

Section: • |mentioning

confidence: 99%

3-D Granulometry Using Image Processing

Labati

Genovese

Muñoz

et al. 2019

IEEE Trans. Ind. Inf.

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Image-based methods for estimating the particle size distribution (granulometry) usually analyze two-dimensional (2D) samples of particles disposed on a conveyor belt. Such approaches have to deal with occlusions and cannot evaluate the thickness of each particle. Three-dimensional (3D) vision systems can reduce the acquisition constraints and speed up the quality control process. This paper proposes a novel 3D vision system for analyzing the granulometry of falling particles. The system is designed to work in real-time and to compute a partial 3D reconstruction of the particle from a single pair of twoview images, which is then enhanced by using a neural-based technique. The validation of the proposed approach has been performed by considering three application scenarios for which the system achieved satisfactory accuracy and robustness.

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Section: • |mentioning

confidence: 99%

3-D Granulometry Using Image Processing

Labati

Genovese

Muñoz

et al. 2019

IEEE Trans. Ind. Inf.

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“…The development of an intelligent classification system based on image recognition (such as the shape, color, size, etc. of the material) can improve work efficiency and has many advantages over manual screening, such as uniform grading standards, reduction of mechanical noise and energy consumption, as well as providing statistical information on various classification indicators [23]- [25].…”

Section: A Explosion Block Degree Classification Image Recognitionmentioning

confidence: 99%

Distribution Characteristics of Fragments Size and Optimization of Blasting Parameters Under Blasting Impact Load in Open-Pit Mine

et al. 2019

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The size distribution characteristics of fragments formed after loose blasting in open-pit mine will directly affect the economic benefits of the mining enterprise. The impact load test of coal samples with pressure ranging from 0.13MPa to 2.0MPa was carried out by using the split Hope Pressure Bar Test System (SHPB). Based on the theory of stress wave attenuation, the relationship between blasting load and coal samples impact test in the laboratory is established. It is concluded that with the increase of impact pressure, the degree of coal samples breakage increases gradually. According to the requirement of blasting quality in open-pit mine, it is concluded that the impact pressure range with good crushing effect is 0.30MPa ≤ P ≤ 0.90MPa. Based on image recognition method, the fragmentation distribution characteristics of loose deposits of coal seam blasting in open-pit mine are obtained. Combining the impact load test in the laboratory and the fragmentation distribution of coal seam blasting, the influence area of blasting impact fragmentation is divided. According to the research results, two optimization schemes of blasting parameters are put forward and the blasting effect of the two schemes is verified by numerical simulation using ANSYS/LSDYNA. Through field blasting optimization test in open-pit mine, it is concluded that increasing blasting hole spacing and row spacing can effectively control dust pollution and increase massive coal rate.INDEX TERMS Open-pit mine, fragments size distribution, impact load, numerical simulation, blasting optimization.

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“…Also, the works in [13], [28] take advantage of the same technology to measure rock fragments. From a different perspective, the work in [29] studied rock granulometry using stereophotogrammetry, demonstrating its capability to obtain more precise measurements than traditional 2D approaches. However, in the case of OSBs, the particles employed are very thin (strands are less than 1mm thick typically), which make it difficult an improvement of the granulometry results using additional 3D information.…”

Section: Related Workmentioning

confidence: 99%

Improving OSB wood panel production by vision-based systems for granulometric estimation

Labati

Genovese

Muñoz

et al. 2015

2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI)

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Oriented Strand Board (OSB) is a kind of engineered wood particle board widely adopted in manufacturing, construction and logistics. The production of OSB panels requires rectangular-shaped wood strands of specific size, arranged in layers to form the so-called "mattress" (mat) and bonded together with glue. The structural properties of the panel rely directly on the layer forming. In particular, the size distribution-namely granulometry-of the strands should fulfill standard measures to reach the mechanical properties expected from the panel. Offline granulometry of particle materials is the most commonly procedure used to evaluate the production process, but it is prone to several drawbacks owing to the manual intervention of human operators. Vision-based systems, instead, allow for performing granulometric analyses in an automatic and contactless manner. We propose a computer vision approach to estimate the granulometry of wood strands. The designed framework analyzes images of a mat of strands placed over a moving conveyor belt, and provides useful information and measurements to enhance the production of OSB panels. Because of the very large amount of wood strands on the mat, particle-overlapping is frequent and represents a main issue for measurement algorithms. In order to overcome this problem, our framework joins image processing and computational intelligence methods, such as edge detection and fuzzy color clustering. We tested the framework with real and synthetic images, useful to variate the conditions of the material's shape. The obtained results demonstrate the ability of our approach to evaluate the granulometry of the strands in real conditions, and robustness against the simulated variations of the production process.

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Statistical estimation of blast fragmentation by applying stereophotogrammetry to block piles

Cited by 13 publications

References 18 publications

3-D Granulometry Using Image Processing

3-D Granulometry Using Image Processing

Distribution Characteristics of Fragments Size and Optimization of Blasting Parameters Under Blasting Impact Load in Open-Pit Mine

Improving OSB wood panel production by vision-based systems for granulometric estimation

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