More Like this

1. Particle Analysis From Heterogeneous Background With Deep Learning Tool

   https://doi.org/10.1115/MSEC2024-124600  

   Alam, Adeeb; Hassan, Md Rafiul; Khoda, Bashir (June 2024, American Society of Mechanical Engineers)

   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. 

   more » « less
2. Effect of Withdrawal Velocity on Particle Entrainment From Density Mismatched Mixture

   https://doi.org/10.1115/MSEC2022-85745  

   Shovon, S. M.; Khalil, Ibrahim; Alam, Adeeb; Khoda, Bashir (June 2022, Proceedings of the ASME 2022 17th International Manufacturing Science and Engineering Conference)

   Abstract In this work, the physical phenomenon of the polydisperse micro-particle entrainment process from density mismatch mixture is investigated with the variation of substrate withdrawal speed. A liquid carrier system (LCS) is prepared by a polymer-based binder and an evaporating solvent. Nickel-based inorganic and spherical particles with a. moderate vol%. of 35% are added to the LCS solution. The cylindrical AISI 1006 mild steel wire substrate is dipped at different withdrawal speed ranging from 0.01 mms-1 to 20 mms-1. The binder vol%. is varied between 6.5% and 10.5%. Once the cylindrical substrate is extracted from the mixture, the surface coverage and the particle size are measured following the image analysis technique. The average particle size, coating thickness and the surface packing coverage by the particles are increasing with the higher withdrawal speed of the substrate. We observed relatively low size of particles (< 10 micrometers) as well as low surface coverage (∼33%) when the withdrawal speed remains at 0.01 mm/s. However, with high withdrawal speed (20 mm/s), we found all sizes of particles present on the substrate with a surface coverage of over 90%. The finding of this research will help to understand the high-volume solid transfer technique and develop a novel manufacturing process. 

   more » « less
3. Diffusion of Anisotropic Colloidal Microparticles Fabricated Using Two‐Photon Lithography

   https://doi.org/10.1002/ppsc.202100033  

   Doan, David; Kulikowski, John; Gu, X. Wendy (July 2021, Particle & Particle Systems Characterization)

   Abstract Polymeric particles with complex shapes are required for biomedical therapies, colloidal self‐assembly, and micro‐robotics. It has been challenging to synthesize particles beyond simple shapes (e.g., spheres, cubes) with high structural accuracy using existing methods. Here, a method for fabricating polymeric microparticles of complex 3D shapes is reported using two‐photon lithography, and dispersing the particles in an aqueous solution on a glass substrate. The fabrication of polyhedrons (e.g., tetrahedron, pyramid), polypods (e.g., tetrapod, hexapod), and other shapes of 5–10 µm in size is demonstrated. Confocal microscopy is used to track the motion of the sphere, tetrahedron, tetrapod, and screw‐shaped particles near the substrate, and determine their translational diffusion coefficients. HYDRO++ is used to simulate the motion of the particles far from the substrate. The influence of particle size and substrate effects on diffusion in the spherical particles is determined and finds that the non‐spherical particles have increased hindrance at the substrate compared to the spherical particles. 

   more » « less
4. Multiple particle tracking microrheology measured using bi-disperse probe diameters

   https://doi.org/10.1039/C8SM01098F  

   Wehrman, Matthew D.; Lindberg, Seth; Schultz, Kelly M. (January 2018, Soft Matter)

   Multiple particle tracking microrheology (MPT) is a powerful tool for quantitatively characterizing rheological properties of soft matter. Traditionally, MPT uses a single particle size to characterize rheological properties. But in complex systems, MPT measurements with a single size particle can characterize distinct properties that are linked to the materials' length scale dependent structure. By varying the size of probes, MPT can measure the properties associated with different length scales within a material. We develop a technique to simultaneously track a bi-disperse population of probe particles. 0.5 and 2 μm particles are embedded in the same sample and these particle populations are tracked separately using a brightness-based squared radius of gyration, R g 2 . Bi-disperse MPT is validated by measuring the viscosity of glycerol samples at varying concentrations. Bi-disperse MPT measurements agree well with literature values. This technique then characterizes a homogeneous poly(ethylene glycol)-acrylate:poly(ethylene glycol)-dithiol gelation. The critical relaxation exponent and critical gelation time are consistent and agree with previous measurements using a single particle. Finally, degradation of a heterogeneous hydrogenated castor oil colloidal gel is characterized. The two particle sizes measure a different value of the critical relaxation exponent, indicating that they are probing different structures. Analysis of material heterogeneity shows measured heterogeneity is dependent on probe size indicating that each particle is measuring rheological evolution of a length scale dependent structure. Overall, bi-disperse MPT increases the amount of information gained in a single measurement, enabling more complete characterization of complex systems that range from consumer care products to biological materials. 

   more » « less
5. In-situ particle analysis with heterogeneous background: a machine learning approach

   https://doi.org/10.1038/s41598-024-59558-7  

   Alam, Adeeb Ibne; Rahman, Md Hafizur; Zia, Akhter; Lowry, Nate; Chakraborty, Prabuddha; Hassan, Md Rafiul; Khoda, Bashir (May 2024, Scientific Reports)

   Abstract We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing. 

   more » « less