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Related Experiment Video

Updated: Nov 28, 2025

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

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A U-Net Based Approach for Automating Tribological Experiments.

Benjamin Staar^1,2, Suleyman Bayrak³, Dominik Paulkowski³

¹BIBA-Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany.

Sensors (Basel, Switzerland)

|November 26, 2020

Summary

This summary is machine-generated.

Automating tribological tests with convolutional neural networks (CNNs) significantly speeds up material development. This AI approach accurately measures contact areas, approaching human performance for efficient material and coating characterization.

Area of Science:

Materials Science
Tribology
Artificial Intelligence

Background:

Tribological experiments are essential for evaluating material performance.

Keywords:

convolutional neural network semantic segmentation tribology

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Manual measurement of contact areas in these tests is time-consuming.

Automating tribological testing can accelerate the development of new materials and coatings.