Image-based identification of optical quality and functional properties in inkjet-printed electronics using machine learning

Maxim Polomoshnov (), Klaus-Martin Reichert, Luca Rettenberger, Martin Ungerer, Gerardo Hernandez-Sosa, Ulrich Gengenbach and Markus Reischl
Additional contact information
Maxim Polomoshnov: Karlsruhe Institute of Technology (KIT)
Klaus-Martin Reichert: Karlsruhe Institute of Technology (KIT)
Luca Rettenberger: Karlsruhe Institute of Technology (KIT)
Martin Ungerer: Karlsruhe Institute of Technology (KIT)
Gerardo Hernandez-Sosa: Karlsruhe Institute of Technology (KIT)
Ulrich Gengenbach: Karlsruhe Institute of Technology (KIT)
Markus Reischl: Karlsruhe Institute of Technology (KIT)

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 24, 2709-2726

Abstract: Abstract We propose a novel image-analysis based machine-learning approach to the fully-automated identification of the optical quality, of functional properties, and of manufacturing parameters in the field of 2D inkjet-printed test structures of conductive traces. To this end, a customizable modular concept to simultaneously identify or predict dissimilar properties of printed functional structures based on images is described and examined. An application domain of the concept in the printing production process is outlined. To examine performance, we develop a dataset of over 5000 test structures containing images and physical characteristics, which are manufactured using commercially available materials. Functional test structures are fabricated via a single-nozzle vector-based inkjet-printing system and thermally sintered. Physical characterization of electrical conductance, image capturing, and evaluation of the optical quality of the test structures is done by an automatic in-house built measurement station. Conceptionally, the design of a convolutional neural network is described to identify the optical quality and physical characteristics based only on acquired images. A mathematical apparatus that allows assessment of the identification accuracy is developed and described. The impact of printing resolution, of emerging defects in the geometry of printed structures, and of image quality and color space on the identification accuracy is analyzed. Quality groups related to the printing resolution that affect identification accuracy are determined. Supplementarily, we introduce not yet reported classification of processes related to the fabrication of printed functional structures, adopted from the process analytical technology.

Keywords: Computer vision; Image analysis; Inkjet printing; Machine learning; Neural network; Printed electronics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02385-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02385-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02385-4

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().