Development of Particle-size Control Technology for Powder Coatings
Abstract
Powder coatings have emerged as an eco-friendly and efficient alternative to traditional liquid coatings, owing to their solvent-free composition and superior performance in impact resistance and corrosion resistance. However, the particle size distribution (PSD) of powder coatings remains a critical challenge, as wide PSDs adversely affect powder flowability, deposition efficiency, and the appearance of the coating films. This dissertation addresses the wide PSDS of powder coatings by introducing innovative modifications to air classifying mill (ACM) and applying advanced analytical methods to explore the impacts of PSD on powder coatings.
The research focuses on narrowing PSDs through two primary interventions. Firstly, self- designed ACM classifiers with serrated edges were developed to improve classification precision, achieving a significant reduction in span values (< 0.2) and enhancing both powder flowability and film quality. Secondly, a novel secondary air guider (SAG) modification for ACM cyclones was introduced, effectively reducing ultra-fine and ultra-large particle fractions, as confirmed by experimental tests and computational fluid dynamics (CFD) simulations.
Beyond system optimization, this study introduces two novel PSD parameters, E5 and E80, to capture the distinct impacts of ultra-fine and ultra-large particles. Comprehensive experiments revealed that E5 strongly correlates with powder flowability and deposition efficiency, while E80 mainly affects thickness uniformity and film performance.
Finally, this research pioneers the application of machine learning in the powder coating domain. Artificial neutral network (ANN) was utilized to systematically quantify the intricate relationships between PSD and the gloss and surface roughness (Ra) of coating films. The analysis revealed that ultra-large particles negatively correlate with gloss and positively correlate with Ra, indicating that ultra-large particles significantly cause gloss loss and increase surface roughness. On the contrary, higher fractions of ultra-fine particles contributed to smoother surface and higher gloss values. These findings aligned with experimental results, highlighting that minimizing ultra-large particles (smaller E80) is essential for enhancing surface aesthetics and achieving smoother coatings.
This dissertation contributes to the powder coating research field by advancing PSD control methodologies, introducing innovative classification techniques, and pioneering the use of machine learning for analysis. These findings offer practical pathways for improving the quality of powder coatings, with implications for broader industrial adoption.