Deep learning based automated quantification of powders used in additive manufacturing

• Published in: Additive manufacturing letters Vol. 11; p. 100241
• Main Authors: Krishna, K.V. Mani, Anantatamukala, A., Dahotre, Narendra B.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024; Elsevier
• Subjects: CNN; Generative adversarial networks; Machine learning; Powder size distribution; Quantification; Generative adversarial networks; CNN; Quantification; Powder size distribution; Machine learning
• ISSN: 2772-3690; 2772-3690
• DOI: 10.1016/j.addlet.2024.100241

•Machine Learning (ML) based segmentation of powder particle imagery achieved.•Overlapping particles could be successfully delineated.•Particle size through ML based technique was within 7 % of standard measurement methods.•Additional shape parameters of circularity and aspect ratio were determined. This study proposes a novel deep learning technique for efficient powder morphology characterization, crucial for successful additive manufacturing. The method segments powder particles in microscopy images using Pix2Pix image translation model, enabling precise quantification of size distribution and extraction of critical morphology parameters like circularity and aspect ratio. The proposed approach achieves high accuracy (Structural Similarity Index of 0.8) and closely matches established methods like laser diffraction in measuring particle size distribution (within a deviation of ∼7 %) and allows determination of additional particle attributes of aspect ratio and circualarity in a reliable, repeated, and automated way. These findings highlight the potential of deep learning for automated powder characterization, offering significant benefits for optimizing additive manufacturing processes. [Display omitted]