High-throughput viscometry via machine-learning from videos of inverted vials
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913871918268416 |
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| author | Arretche, Ignacio Hossain, Mohammad Tanver Tiwari, Ramdas Kim, Abbie Mills, Mya G. Armstrong, Connor D. Lessard, Jacob J. Tawfick, Sameh H. Ewoldt, Randy H. |
| author_facet | Arretche, Ignacio Hossain, Mohammad Tanver Tiwari, Ramdas Kim, Abbie Mills, Mya G. Armstrong, Connor D. Lessard, Jacob J. Tawfick, Sameh H. Ewoldt, Randy H. |
| contents | Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted vial test and enables quantitative viscosity inference across nearly five orders of magnitude (0.01-1000 Pas), without requiring direct velocity field measurements. The system simultaneously inverts multiple vials and records videos of the evolving fluid, which are fed into a neural network that approximates the inverse function from visual features and known fluid density. Despite the complex, multi-regime flow within the vial, our approach achieves relative errors below 25%, improving to 15% for viscosities above 0.1 Pas. When tested on non-Newtonian polymer solutions, the method reliably estimates zero-shear viscosity as long as viscoelastic or shear-thinning behaviors remain negligible within the flow regime. Moreover, high standard deviations in the inferred values may serve as a proxy for identifying fluids with strong non-Newtonian behavior. The CV viscometer requires only one camera and one motor, is contactless and low-cost, and can be easily integrated into high-throughput experimental automated and manual workflows. Transcending traditional characterization paradigms, our method leverages uncontrolled flows and visual features to achieve simplicity and scalability, enabling high-throughput viscosity inference that can meet the growing demand of data-driven material models while remaining accessible to lower resource environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_02034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-throughput viscometry via machine-learning from videos of inverted vials Arretche, Ignacio Hossain, Mohammad Tanver Tiwari, Ramdas Kim, Abbie Mills, Mya G. Armstrong, Connor D. Lessard, Jacob J. Tawfick, Sameh H. Ewoldt, Randy H. Graphics Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted vial test and enables quantitative viscosity inference across nearly five orders of magnitude (0.01-1000 Pas), without requiring direct velocity field measurements. The system simultaneously inverts multiple vials and records videos of the evolving fluid, which are fed into a neural network that approximates the inverse function from visual features and known fluid density. Despite the complex, multi-regime flow within the vial, our approach achieves relative errors below 25%, improving to 15% for viscosities above 0.1 Pas. When tested on non-Newtonian polymer solutions, the method reliably estimates zero-shear viscosity as long as viscoelastic or shear-thinning behaviors remain negligible within the flow regime. Moreover, high standard deviations in the inferred values may serve as a proxy for identifying fluids with strong non-Newtonian behavior. The CV viscometer requires only one camera and one motor, is contactless and low-cost, and can be easily integrated into high-throughput experimental automated and manual workflows. Transcending traditional characterization paradigms, our method leverages uncontrolled flows and visual features to achieve simplicity and scalability, enabling high-throughput viscosity inference that can meet the growing demand of data-driven material models while remaining accessible to lower resource environments. |
| title | High-throughput viscometry via machine-learning from videos of inverted vials |
| topic | Graphics |
| url | https://arxiv.org/abs/2506.02034 |