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Updated: Jun 25, 2026

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Data-Driven Slip Prediction in Web Processing Machines Using Virtual Sensors and Ensemble Machine Learning.

Colin Soete¹, Jonas Van Der Donckt¹, Nathan Vandemoortele¹

¹IDLab, Department of Electronics and Information Systems, Ghent University-Imec, Technologiepark-Zwijnaarde 122, 9052 Zwijnaarde, Belgium.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

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This summary is machine-generated.

A new virtual slip sensor estimates web slippage in roll-to-roll manufacturing using existing machine data. This cost-effective solution enhances product quality and increases throughput by enabling real-time monitoring without expensive physical sensors.

Area of Science:

Manufacturing Engineering
Control Systems Engineering
Machine Learning

Background:

Roll-to-roll (R2R) systems rely on traction rollers for precise web velocity control.
Web slip during acceleration/deceleration causes tension loss and product defects.
Direct slip detection via encoders is costly and impractical for industrial settings.

Purpose of the Study:

Develop a cost-effective virtual slip sensor for R2R systems.
Estimate web slip using only existing machine signals.
Avoid the need for expensive, permanently installed physical sensors.

Main Methods:

Implemented a virtual slip sensor using an ensemble modeling approach.
Trained predictive models with ground-truth data from a temporary encoder.

Keywords:

ensemble learning predictive maintenance roll-to-roll manufacturing slip prediction virtual sensor web winding machine

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Last Updated: Jun 25, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Published on: October 27, 2016

Combined CatBoost for low-slip and linear models for high-slip extrapolation.

Utilized targeted feature engineering for generalization.

Main Results:

The virtual sensor accurately estimates slip, even under severe, out-of-distribution conditions.
Ensemble model reduced Mean Squared Error (MSE) by up to 68% compared to a standard CatBoost model.
Achieved robust predictive performance across varying ramp times and web speeds.
Demonstrated effective generalization from limited-slip training data.

Conclusions:

The virtual slip sensor provides continuous, real-time monitoring of web slip.
Enables operators to prevent quality degradation and increase throughput.
Offers a practical, scalable solution for industrial R2R manufacturing.