In‐line measurement and characterization in glass bead‐filled polypropylene extrusion based on machine vision

Abstract Particle filling is a typical method for reinforcing polymer matrix materials, and the properties of particle filler have a significant impact on the performance of polymeric materials. However, there is still a lack of effective in situ measurement and characterization methods of particulate‐filled polymer. In this paper, an in‐line measurement and characterization method for glass bead‐filled polypropylene (GB/PP) based on machine vision was proposed. A visual die was developed, and the polymer melt in the visual area was photographed by a high‐speed camera to obtain images for analysis. An improved YOLO target detection algorithm was applied to identify particles in images. The particle size distribution, dispersion, and component content of glass beads in the polypropylene were measured and characterized through statistical analysis, and then verified by other off‐line methods. These properties calculated by our method agree well with those measured by other off‐line methods. This method can achieve the in‐line measurement and characterization for GB/PP extrusion, which is promising for the in‐line property monitoring of particulate‐filled polymers in the industry.