Ore Particle Size Histogram Detection and Porosity Analysis Calculation

Ore particle size histogram detection and porosity analysis calculation are crucial steps in sintered metal materials research. The analysis method based on metallographic images enables efficient evaluation of material microstructure characteristics, providing data support for material performance optimization.

Ore Particle Size Histogram Detection Utilizing image processing techniques, the method extracts contour information of ore particles from metallographic photos and generates histograms by measuring particle size distribution. The process typically involves image preprocessing (such as denoising, binarization), edge detection, and particle segmentation, ultimately counting particles in different size ranges to form size distribution data. Code implementation approach: This can be achieved using OpenCV functions like GaussianBlur() for noise reduction, threshold() for binarization, and findContours() for particle boundary extraction. The particle sizing algorithm would measure contour areas or equivalent diameters using contourArea() or custom diameter calculation functions.

Porosity Analysis Calculation Porosity is a key indicator for measuring the density of sintered materials, calculated based on pixel analysis of metallographic images. Threshold segmentation distinguishes pore regions from the metal matrix, calculating the area proportion occupied by pores as porosity. To improve accuracy, morphological processing can optimize pore contours, and using average values from multiple images reduces errors. Algorithm explanation: The implementation typically uses adaptiveThreshold() or Otsu's thresholding for automatic pore segmentation. Morphological operations like morphologyEx() with opening/closing kernels help refine pore boundaries. Porosity calculation formula: Porosity = (Pore Pixel Count / Total Pixel Count) × 100%.

Application and Optimization This method is widely used in powder metallurgy, ceramic sintering, and other fields to help optimize process parameters. Combined with machine learning algorithms, it can further enhance analysis efficiency, making it suitable for batch processing of metallographic image data. Key function enhancement: Integration with scikit-learn for automated classification or CNN-based segmentation (using U-Net architecture) can improve particle identification accuracy and processing speed for large datasets.