ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Process

• URL: http://arxiv.org/abs/2512.01268v2
• Date: Tue, 02 Dec 2025 02:57:33 GMT
• Title: ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Process
• Authors: Jongwon Sohn, Juhyeon Moon, Hyunjoon Jung, Jaewook Nam,
• Abstract summary: We present a computer-vision-based viscometer that infers viscosity by exploiting how a fixed background pattern becomes optically distorted as light refracts through the mixing-driven, continuously deforming free surface.<n>The system achieves a mean absolute error of 0.113 in log m2 s-1 units for regression and reaches up to 81% accuracy in viscosity-class prediction.
• Score: 3.8744272299940903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Viscosity measurement is essential for process monitoring and autonomous laboratory operation, yet conventional viscometers remain invasive and require controlled laboratory environments that differ substantially from real process conditions. We present a computer-vision-based viscometer that infers viscosity by exploiting how a fixed background pattern becomes optically distorted as light refracts through the mixing-driven, continuously deforming free surface. Under diverse lighting conditions, the system achieves a mean absolute error of 0.113 in log m2 s^-1 units for regression and reaches up to 81% accuracy in viscosity-class prediction. Although performance declines for classes with closely clustered viscosity values, a multi-pattern strategy improves robustness by providing enriched visual cues. To ensure sensor reliability, we incorporate uncertainty quantification, enabling viscosity predictions with confidence estimates. This stand-off viscometer offers a practical, automation-ready alternative to existing viscometry methods.

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