Particle shape characterizations for energetic materials by computational geometry and stereology method

Abstract

Particle shapes significantly affect viscosity and flow behavior of energetic materials, and therefore affect their packability and processability. This study presents a computational geometry framework for automatically quantifying two-dimensional (2D) and three-dimensional (3D) particle shapes of energetic materials. A specimen by mixing three typical energetic materials including HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), RDX (1,3,5-Trinitroperhydro-1,3,5-triazine) and AP (Ammonium Perchlorate) particles is used in this study. This specimen is scanned by high-resolution X-ray computed tomography (X-ray CT), yielding a volumetric image. An improved watershed analysis algorithm is used to process the volumetric image to identify individual 3D particles. The stereology sampling method is used to obtain 2D projections of 3D particles. Computational geometry techniques are developed by this study to analyze 2D particle projections and 3D particle geometries to compute seven commonly used shape descriptors, including convexity, circularity, intercept sphericity, area sphericity, diameter sphericity, circle ratio sphericity, and surface area sphericity. Results show that those different shape descriptors of energetic materials can be divided into three groups based on their numerical ranges. This study also evaluates the effectiveness and accuracy of 2D shape descriptors for quantifying the true 3D shapes. The inconsistent characterization results between 2D and 3D shape descriptors suggest that researchers should be cautious when using 2D images to characterize 3D particle shapes of energetic materials. The computational geometry framework and particle shape analysis results presented in this study can be potentially useful in numerical modeling, experimental analysis, and theoretical investigation for energetic materials.