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Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms

cris.lastimport.scopus2025-10-23T06:56:58Z
cris.virtual.author-orcid0000-0002-9588-2514
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcidae71bc22-fde2-40b2-878c-e07e0e5aad5a
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enCorrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze five-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring the layer and overall board thickness, flute height, and center lines of each layer. Through the integration of image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method only faced limitations with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement in order to enhance robustness and accuracy.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorRogalka, Maciej
dc.contributor.authorGrabski, Jakub Krzysztof
dc.contributor.authorGarbowski, Tomasz
dc.date.access2025-08-07
dc.date.accessioned2025-08-07T06:05:12Z
dc.date.available2025-08-07T06:05:12Z
dc.date.copyright2024-03-09
dc.date.issued2024
dc.description.abstract<jats:p>Corrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze five-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring the layer and overall board thickness, flute height, and center lines of each layer. Through the integration of image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method only faced limitations with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement in order to enhance robustness and accuracy.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,5
dc.description.number6
dc.description.points100
dc.description.versionfinal_published
dc.description.volume24
dc.identifier.doi10.3390/s24061772
dc.identifier.issn1424-8220
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4103
dc.identifier.weblinkhttp://www.mdpi.com/1424-8220/24/6/1772
dc.languageen
dc.relation.ispartofSensors
dc.relation.pagesart. 1772
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.encorrugated board
dc.subject.endouble-walled
dc.subject.enflute parameters
dc.subject.encross-section images
dc.subject.engenetic algorithm
dc.titleDeciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume24