Sorting of Poly-Disperse Particle by Entrapment Using Liquid Carrier System

Khalil, Ibrahim; Khoda, Bashir

doi:10.1115/1.4052440

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…2 . For example, in our recent work we developed a poly-disperse inorganic particle sorting process by entrapment onto a cylindrical rod 34 . We used Ni-based brazing powder (Nicrobraz 51; Wall Colmonoy company, Ohio; Spherical dia range ~ 0–100 µm) as bulk particles.…”

Section: Introductionmentioning

confidence: 99%

In-situ particle analysis with heterogeneous background: a machine learning approach

Alam,

Rahman,

Zia

et al. 2024

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

In-situ particle analysis with heterogeneous background: a machine learning approach

Alam,

Rahman,

Zia

et al. 2024

Sci Rep

Self Cite

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“…By understanding their transfer physics from multi-phase fluid can open-up an economical manufacturing process for complex architecture including porous solid 20 , 21 . Such understanding can be directly implemented to novel particle sorting technique of different size and density distribution 39 , 40 , inorganic material coating on 3D printed polymer surface 41 , acoustic meta-surface for hypersonic boundary layer transition and stabilization 42 , dust mitigation in both hydrophobic and hydrophilic surfaces 43 surface wettability and flowability improvement for medical devices 44 and thin-surface wicks for heat and mass transfer 45 – 47 . The micro-scale inorganic particles have reduced surface area and higher density, making them negatively buoyant in the LCS and challenging to yield high solid transfer due to the density mismatch causing them to settle in suspension.…”

Section: Introductionmentioning

confidence: 99%

Micro-particle entrainment from density mismatched liquid carrier system

Shovon

Alam

Gramlich

et al. 2022

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Micro-scale inorganic particles (d > 1 µm) have reduced surface area and higher density, making them negatively buoyant in most dip-coating mixtures. Their controlled delivery in hard-to-reach places through entrainment is possible but challenging due to the density mismatch between them and the liquid matrix called liquid carrier system (LCS). In this work, the particle transfer mechanism from the complex density mismatching mixture was investigated. The LCS solution was prepared and optimized using a polymer binder and an evaporating solvent. The inorganic particles were dispersed in the LCS by stirring at the just suspending speed to maintain the pseudo suspension characteristics for the heterogeneous mixture. The effect of solid loading and the binder volume fraction on solid transfer has been reported at room temperature. Two coating regimes are observed (i) heterogeneous coating where particle clusters are formed at a low capillary number and (ii) effective viscous regime, where full coverage can be observed on the substrate. ‘Zero’ particle entrainment was not observed even at a low capillary number of the mixture, which can be attributed to the presence of the binder and hydrodynamic flow of the particles due to the stirring of the mixture. The critical film thickness for particle entrainment is $${h}^{*}=0.16a$$ h ∗ = 0.16 a for 6.5% binder and $${h}^{*}=0.26a$$ h ∗ = 0.26 a for 10.5% binder, which are smaller than previously reported in literature. Furthermore, the transferred particle matrices closely follow the analytical expression (modified LLD) of density matching suspension which demonstrate that the density mismatch effect can be neutralized with the stirring energy. The findings of this research will help to understand this high-volume solid transfer technique and develop novel manufacturing processes.

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