Abstract
The performance of conventional image processing techniques is highly dependent on many parameters like image quality, light source, background surface texture, optimal threshold value and particle morphology. However, during intermediate stages of manufacturing processes (such as continuous deposition, coating, mixing, and transfer), complex backgrounds can arise from heterogeneous particle-substrate (HPS) systems. In such HPS environments, particles become integrated with substrates or suspended in liquid carriers or etching media, making them challenging to identify using traditional particle analysis tools and techniques. In response to this challenge, a deep learning object detection algorithm (YOLO) has been put into practical use. Initially, an HPS (heterogeneous particle-substrate) system was created using a wet-deposition particle transfer process that involved the immersion of poly-disperse particles on to a cylindrical substrate. By manipulating the capillary number in the wet-deposition process, four distinct HPS morphologies were captured, each characterized by variations in image heterogeneity. These morphologies were subsequently subjected to detailed analysis with neural network-based AI algorithm. The proposed artificial intelligence tool has demonstrated an impressive ability to identify and analyze poly-dispersed particles within HPS morphologies, achieving an accuracy rate of over 97%. We can evaluate the quality of sorting by calculating the particle size distribution using the proposed method and find the ideal process parameters for the particle transfer process. The results of this study, outlined in this paper, underscore the potential of deep learning as a particle analysis tool for in-situ applications, even in environments with heterogeneous backgrounds. This developed tool holds promise for various manufacturing processes, including semiconductor industries, high-density powder-based 3D printing, powder metallurgy, refractory coatings in harsh environments, and particle sorting, among others.
