Systematic Variation of Friction of Rods

Khalil, Md. Ibrahim; Tong, Dezhong; Wang, Guanjin; Jawed, Mohammad; Khoda, Bashir

doi:10.1115/1.4055544

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…The entrained or entrapped particle on the substrate often needs to be characterized in-situ before they can be sent to the next step of the manufacturing or coating process. For example, in our recent work, we used the entrained particles on an elastic rod to control the friction coefficient 36 , 37 . By analyzing the entrained particles, we can predict and hence control the friction coefficient for various applications including robot grippers, knot design, wire entanglement etc.…”

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

View full text Add to dashboard Cite

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Mechanical Response of Fisherman’s Knots During Tightening

Tong,

Ibrahim Khalil,

Justin Silva

et al. 2023

Journal of Applied Mechanics

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The fisherman's knot, renowned for its strength and reliability, finds applications in engineering and medicine. However, a comprehensive understanding of its mechanics remains limited in scientific literature. In this paper, we present a systematic study of the tightening behavior of the fisherman's knot through a combined approach of tabletop experiments and Discrete Elastic Rods simulations. Our experimental setup involves gradually applying tension to the two ends of the fisherman's knot until it fractures. We observed a correlation between the knot's material properties and its behavior during tightening, leading up to fracture. The tightening process of the fisherman's knot exhibits distinct ``sliding' or ``stretching' motions, influenced by factors such as friction and elastic stiffness. Furthermore, the failure modes of the knot (material fracture and topological failure) are determined by an interplay between elastic stiffness, friction, and initial conditions. This study sheds light on the underlying mechanics of the fisherman's knot and provides insight into its behavior during the tightening process, contributing to the broader understanding of the mechanics of knots in practical applications.

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Controlling Surface of Rods With Entrained Particle as Asperities

Khalil,

Islam,

Tong

et al. 2023

Journal of Micro- And Nano-Manufacturing

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Changing the surface properties (i.e., roughness or friction) can be instrumental for many applications but can be a complex and resources intensive process. In this paper, we demonstrate a novel process of controlling the friction of a continuous rod by delivering inorganic micro-particles. A standardized continuous particle transfer protocol has been developed in our laboratory for depositing particles from a liquid carrier system to the cylindrical rod substrate. The particle transfer process can produce controllable and tunable surface properties. Polymeric binder is used to deliver the particles as asperities over the rod substrate and by controlling their size, shape and distribution, the coefficient of friction of the rod is determined. Tabletop experiments are designed and performed to measure the friction coefficient following the Capstan equation. The entrained particles on the substrate will create a size and shape-based asperities which will alter the surface morphology toward desired direction. Both oblique and direct quantitative measurements are performed at different particles and binder concentrations. A systematic variation in the friction coefficient is observed and reported in the result section. It is observed from the capstan experiment that adding only 1% irregular shaped particles in the suspension changes the friction coefficient of the rods by almost 115%. The proposed friction control technique is a simple to scale up, low-cost, low-waste, and low energy manufacturing method for controlling the surface morphology.

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Systematic Variation of Friction of Rods

Cited by 3 publications

References 46 publications

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

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

Mechanical Response of Fisherman’s Knots During Tightening

Controlling Surface of Rods With Entrained Particle as Asperities

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