Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing Article Swipe

MAKE IMPACT

Pauline Brumm , Steven L. Brunton , Isabel Scherl · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1088/3049-4761/adf115

Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood. However, fundamental understanding is essential to achieve high-quality printed products and to tune printed patterns according to the needs of a specific application like printed electronics, graphical printing, or biomedical printing. The aim of the paper is to develop an automated pattern classification algorithm based on methods from supervised machine learning and reduced-order modeling. We use the HYPA-p dataset, a large image dataset of gravure-printed images, which shows various types of hydrodynamic pattern formation phenomena. It enables the correlation of printing process parameters and resulting printed patterns for the first time. A total of 26 880 images of the HYPA-p dataset have been labeled by a human observer as dot patterns, mixed patterns, or finger patterns; 864 000 images (97%) are unlabeled. A singular value decomposition is used to find the modes of the labeled images and to reduce the dimensionality of the full dataset by truncation and projection. Selected machine learning classification techniques are trained on the reduced-order data. We investigate the effect of several factors, including classifier choice, whether or not fast Fourier transform (FFT) is used to preprocess the labeled images, data balancing, and data normalization. The best performing model is a k-nearest neighbor (kNN) classifier trained on unbalanced, FFT-transformed data with a test error of 3%, which outperforms a human observer by 7%. Data balancing slightly increases the test error of the kNN-model to 5%, but also increases the recall of the mixed class from 90% to 94%. Finally, we demonstrate how the trained models can be used to predict the pattern class of unlabeled images and how the predictions can be correlated to the printing process parameters, in the form of regime maps.

Type

article

Language

en

Landing Page

https://doi.org/10.1088/3049-4761/adf115

OA Status

hybrid

References

27

Related Works

10

OpenAlex ID

https://openalex.org/W4413403461

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4413403461

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.1088/3049-4761/adf115

Digital Object Identifier

Title

Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing

Work title

Type

article

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-08-21

Full publication date if available

Authors

Pauline Brumm, Steven L. Brunton, Isabel Scherl

List of authors in order

Landing page

https://doi.org/10.1088/3049-4761/adf115

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

hybrid

Open access status per OpenAlex

OA URL

https://doi.org/10.1088/3049-4761/adf115

Direct OA link when available

Concepts

Order (exchange), Computer science, Process engineering, Engineering drawing, Materials science, Engineering, Business, Finance

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

References (count)

27

Number of works referenced by this work

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload