Direct measurement of spreadability and surface quality of powder beds using advanced image analysis for additive manufacturing

Powder bed quality plays a decisive role in the performance and reliability of components produced by powder based additive manufacturing (AM). Conventional surface assessment methods, however, often lack the sensitivity to detect subtle local defects and textural variations that influence layer uniformity. This study present a direct and quantitative approach to evaluating powder bed spreadability and surface quality using a custom-built recoating tester operating under conditions representative of industrial AM systems. High-resolution images of powder beds generated from a range of polymeric and metallic powders were analysed using advanced texture metrics, including Haralick features, Gabor filters, and local binary patterns (LBP). These image-derived metrics proved highly sensitive to micro-defects, surface roughness and variations associated with particle size distribution, morphology and flow behaviour. Multivariate analysis further demonstrated that surface quality is governed by the combined influence of powder properties and spreading dynamics. The proposed methodology provides a robust, surface-sensitive framework for quantitative powder characterisation and reliable detection of powder bed defects, offering strong potential for future integration into real-time monitoring and process optimisation strategies in AM.