{"title":"VOX-STORM:基于双体素-网格结构的随机三维模型,用于聚合体的形态表征","authors":"L. Théodon , J. Debayle , C. Coufort-Saudejaud","doi":"10.1016/j.powtec.2024.119983","DOIUrl":null,"url":null,"abstract":"<div><p>Measuring the 3D morphological properties of granular objects such as aggregates is a critical issue in many fields of science and industry, especially when the objects are fragile or hard to sample. For these reasons, non-invasive techniques based on image analysis are being developed. However, most image analysis techniques can only measure 2D properties. This paper presents a new approach based on both image analysis and a 3D stochastic geometric model called VOX-STORM (VOXel-based STOchastic geometRical Model) to estimate 3D morphological properties. By adjusting the parameters of the model, the latter is able to generate populations of objects whose 2D property distributions match those measured by image analysis, and to predict 3D morphological property distributions. The model is based on a dual architecture combining voxelized structure and alpha-shape meshing of the external surface, which makes object generation extremely fast (about 1000 objects in 20 s), while allowing rapid computation of 3D characteristics. The method is validated twice, first on 3D printed aggregates and then on a population of 40,000 synthetic aggregates, with mean errors of less than 2.5% in all cases and less than 1% for 2D properties. It is then applied to two sets of images of latex aggregates captured by a morphogranulometer. The morphological property distributions and fractal dimensions are compared to ground truth in the 2D case and to laser diffraction measurements in the 3D case. The results are also compared with two other recent stochastic geometric models, and the VOX-STORM model outperforms them in all scenarios, as well as in speed of execution, while agreeing with experimental measurements. Finally, directions for future work are suggested.</p></div>","PeriodicalId":407,"journal":{"name":"Powder Technology","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0032591024006260/pdfft?md5=608cfe5fc02bb5d40eeab25b68d24826&pid=1-s2.0-S0032591024006260-main.pdf","citationCount":"0","resultStr":"{\"title\":\"VOX-STORM: A stochastic 3D model based on a dual voxel-mesh architecture for the morphological characterization of aggregates\",\"authors\":\"L. Théodon , J. Debayle , C. Coufort-Saudejaud\",\"doi\":\"10.1016/j.powtec.2024.119983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Measuring the 3D morphological properties of granular objects such as aggregates is a critical issue in many fields of science and industry, especially when the objects are fragile or hard to sample. For these reasons, non-invasive techniques based on image analysis are being developed. However, most image analysis techniques can only measure 2D properties. This paper presents a new approach based on both image analysis and a 3D stochastic geometric model called VOX-STORM (VOXel-based STOchastic geometRical Model) to estimate 3D morphological properties. By adjusting the parameters of the model, the latter is able to generate populations of objects whose 2D property distributions match those measured by image analysis, and to predict 3D morphological property distributions. The model is based on a dual architecture combining voxelized structure and alpha-shape meshing of the external surface, which makes object generation extremely fast (about 1000 objects in 20 s), while allowing rapid computation of 3D characteristics. The method is validated twice, first on 3D printed aggregates and then on a population of 40,000 synthetic aggregates, with mean errors of less than 2.5% in all cases and less than 1% for 2D properties. It is then applied to two sets of images of latex aggregates captured by a morphogranulometer. The morphological property distributions and fractal dimensions are compared to ground truth in the 2D case and to laser diffraction measurements in the 3D case. The results are also compared with two other recent stochastic geometric models, and the VOX-STORM model outperforms them in all scenarios, as well as in speed of execution, while agreeing with experimental measurements. 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VOX-STORM: A stochastic 3D model based on a dual voxel-mesh architecture for the morphological characterization of aggregates
Measuring the 3D morphological properties of granular objects such as aggregates is a critical issue in many fields of science and industry, especially when the objects are fragile or hard to sample. For these reasons, non-invasive techniques based on image analysis are being developed. However, most image analysis techniques can only measure 2D properties. This paper presents a new approach based on both image analysis and a 3D stochastic geometric model called VOX-STORM (VOXel-based STOchastic geometRical Model) to estimate 3D morphological properties. By adjusting the parameters of the model, the latter is able to generate populations of objects whose 2D property distributions match those measured by image analysis, and to predict 3D morphological property distributions. The model is based on a dual architecture combining voxelized structure and alpha-shape meshing of the external surface, which makes object generation extremely fast (about 1000 objects in 20 s), while allowing rapid computation of 3D characteristics. The method is validated twice, first on 3D printed aggregates and then on a population of 40,000 synthetic aggregates, with mean errors of less than 2.5% in all cases and less than 1% for 2D properties. It is then applied to two sets of images of latex aggregates captured by a morphogranulometer. The morphological property distributions and fractal dimensions are compared to ground truth in the 2D case and to laser diffraction measurements in the 3D case. The results are also compared with two other recent stochastic geometric models, and the VOX-STORM model outperforms them in all scenarios, as well as in speed of execution, while agreeing with experimental measurements. Finally, directions for future work are suggested.
期刊介绍:
Powder Technology is an International Journal on the Science and Technology of Wet and Dry Particulate Systems. Powder Technology publishes papers on all aspects of the formation of particles and their characterisation and on the study of systems containing particulate solids. No limitation is imposed on the size of the particles, which may range from nanometre scale, as in pigments or aerosols, to that of mined or quarried materials. The following list of topics is not intended to be comprehensive, but rather to indicate typical subjects which fall within the scope of the journal's interests:
Formation and synthesis of particles by precipitation and other methods.
Modification of particles by agglomeration, coating, comminution and attrition.
Characterisation of the size, shape, surface area, pore structure and strength of particles and agglomerates (including the origins and effects of inter particle forces).
Packing, failure, flow and permeability of assemblies of particles.
Particle-particle interactions and suspension rheology.
Handling and processing operations such as slurry flow, fluidization, pneumatic conveying.
Interactions between particles and their environment, including delivery of particulate products to the body.
Applications of particle technology in production of pharmaceuticals, chemicals, foods, pigments, structural, and functional materials and in environmental and energy related matters.
For materials-oriented contributions we are looking for articles revealing the effect of particle/powder characteristics (size, morphology and composition, in that order) on material performance or functionality and, ideally, comparison to any industrial standard.