skip to main content


Title: Size-Based Filtration of Poly-Disperse Micro-Particle by Dipping
Abstract

In manufacturing industries, spherical micro-particles are commonly used as (e.g., brazing powder, metal filler, and 3D printing powder) which are produced with droplet-based particle fabrication techniques. Such processes create spherical morphology but introduce polydispersity and follow a continuous exponential pattern commonly expressed with Rosin-Rammler expression. Sorting those micro-particles in a narrower size range is an important but difficult, costly, and challenging process. Here we demonstrate the successful separation of the particles from a poly-disperse mixture with a particle volume fraction of 10% by dipping process. Nickel-based micro-particles (avg. dia. 5.69 μm) are added in a binder-based liquid carrier system. To encounter the gravitational force, external kinetic energy in the form of agitation is applied to ensure the uniform dispersion of the particles. The cylindrical substrate is prepared and dipped in the ‘pseudo suspension’ to separate the particles by adhering to it. The substrate is dried, and images are taken to characterize the separated particles using image J software. A clear size distribution can be observed which is also plotted. Additionally, a relationship between the process parameter and sorted particles has been established. The proposed method is quick, controllable, and easy to implement, which can be a useful tool for sorting wide-range poly-disperse particles.

 
more » « less
Award ID(s):
2101745
NSF-PAR ID:
10466310
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the ASME 2022 17th International Manufacturing Science and Engineering Conference
Volume:
1
Page Range / eLocation ID:
V001T07A017
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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
  2. 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
  3. 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
  4. 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
  5. ABSTRACT Bioinspired micromanipulators have been made based on gecko dynamic self-cleaning mechanism. Various particles such as spherical SiO 2 /polystyrene, and short fibrous glass can be captured, transmitted and dropped on glass substrate with precisely predesigned patterns, by using the micromanipulator with the help of atomic force microscope (AFM). It has been demonstrated that particle-pad interface and particle-substrate interface exhibit diverse adhesion behaviors under different z-piezo retracting speed. The particle-substrate adhesion increases faster than the particle-pad adhesion with increasing the detaching velocity, which makes it possible to manipulate the particles by adjusting the retreating speed only. Probability tests was performed to better choose suitable parameters for picking and dropping operations. This work provides a potential solution to manipulation of micro/nano particles for precise assembly. 
    more » « less