Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP

This study introduces MIRANDA, a computer vision system, and RELAPP, a complementary force measurement system, developed for characterizing viscoelastic materials. Our aim was to evaluate their combined ability to predict key rheological parameters and demonstrate their utility in material analysis,...

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Autores: Monleón Getino, Toni, Madarnás-Gómez, Victor, Cobos-Soler, Mario, Almacellas, Eduard, Ramos-Castro, Juan, Bielsa, Xavier, López-Brosa, Pere, Sahuquillo Estrugo, Àngels, Marsà-González, Inés, Rodríguez-Mena, Alejandro
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/226038
Acceso en línea:https://hdl.handle.net/2445/226038
Access Level:acceso abierto
Palabra clave:Viscositat
Robòtica
Visió per ordinador
Viscosity
Robotics
Computer vision
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spelling Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPPMonleón Getino, ToniMadarnás-Gómez, VictorCobos-Soler, MarioAlmacellas, EduardRamos-Castro, JuanBielsa, XavierLópez-Brosa, PereSahuquillo Estrugo, ÀngelsMarsà-González, InésRodríguez-Mena, AlejandroViscositatRobòticaVisió per ordinadorViscosityRoboticsComputer visionThis study introduces MIRANDA, a computer vision system, and RELAPP, a complementary force measurement system, developed for characterizing viscoelastic materials. Our aim was to evaluate their combined ability to predict key rheological parameters and demonstrate their utility in material analysis, offering an alternative to traditional methods. We analyzed five distinct flour dough samples, correlating MIRANDA and RELAPP variables with established rheological reference values. Support Vector Machine (SVM) regression models were trained using MIRANDA’s stable TR and elasticity data to predict industrially relevant parameters: baking strength (W), tenacity (P), extensibility (L), and final viscosity (RVU) from Chopin alveograph and viscosimeter. The predictive models showed promising results, with R2 values of 0.594 (p = 0) forW, 0.575 (p = 0) for P, and 0.612 (p = 0.03763) for viscosity, all statistically significant. While these findings are promising, it is important to note that the small sample size may limit the generalizability of these models. The synergy between the systems was evident, exemplified by strong positive correlations, such as between MIRANDA’s Elasticity and RELAPP’s c_exp (parameter ‘c’ of its mathematical model m1, r = 0.858) and final resistive force (r = 0.839). Despite the limited sample size, these findings highlight MIRANDA’s versatility and speed for efficient material characterization. MIRANDA and RELAPP offer significant industrial implications for viscoelastic materials, including accelerating development cycles and enhancing continuous quality control. This approach has strong potential to reduce reliance on slower, traditional methods, warranting further validation with larger datasets.MDPI2026202620252026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion27 p.application/pdfhttps://hdl.handle.net/2445/226038Articles publicats en revistes (Genètica, Microbiologia i Estadística)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3390/ma18214827Materials, 2025, vol. 18, p. 4827https://doi.org/10.3390/ma18214827cc-by (c) Monleón-Getino,Antonio et al., 2025http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2260382026-05-29T05:05:01Z
dc.title.none.fl_str_mv Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
title Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
spellingShingle Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
Monleón Getino, Toni
Viscositat
Robòtica
Visió per ordinador
Viscosity
Robotics
Computer vision
title_short Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
title_full Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
title_fullStr Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
title_full_unstemmed Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
title_sort Advancing Viscoelastic Material Characterization ThroughComputer Vision and Robotics: MIRANDA and RELAPP
dc.creator.none.fl_str_mv Monleón Getino, Toni
Madarnás-Gómez, Victor
Cobos-Soler, Mario
Almacellas, Eduard
Ramos-Castro, Juan
Bielsa, Xavier
López-Brosa, Pere
Sahuquillo Estrugo, Àngels
Marsà-González, Inés
Rodríguez-Mena, Alejandro
author Monleón Getino, Toni
author_facet Monleón Getino, Toni
Madarnás-Gómez, Victor
Cobos-Soler, Mario
Almacellas, Eduard
Ramos-Castro, Juan
Bielsa, Xavier
López-Brosa, Pere
Sahuquillo Estrugo, Àngels
Marsà-González, Inés
Rodríguez-Mena, Alejandro
author_role author
author2 Madarnás-Gómez, Victor
Cobos-Soler, Mario
Almacellas, Eduard
Ramos-Castro, Juan
Bielsa, Xavier
López-Brosa, Pere
Sahuquillo Estrugo, Àngels
Marsà-González, Inés
Rodríguez-Mena, Alejandro
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Viscositat
Robòtica
Visió per ordinador
Viscosity
Robotics
Computer vision
topic Viscositat
Robòtica
Visió per ordinador
Viscosity
Robotics
Computer vision
description This study introduces MIRANDA, a computer vision system, and RELAPP, a complementary force measurement system, developed for characterizing viscoelastic materials. Our aim was to evaluate their combined ability to predict key rheological parameters and demonstrate their utility in material analysis, offering an alternative to traditional methods. We analyzed five distinct flour dough samples, correlating MIRANDA and RELAPP variables with established rheological reference values. Support Vector Machine (SVM) regression models were trained using MIRANDA’s stable TR and elasticity data to predict industrially relevant parameters: baking strength (W), tenacity (P), extensibility (L), and final viscosity (RVU) from Chopin alveograph and viscosimeter. The predictive models showed promising results, with R2 values of 0.594 (p = 0) forW, 0.575 (p = 0) for P, and 0.612 (p = 0.03763) for viscosity, all statistically significant. While these findings are promising, it is important to note that the small sample size may limit the generalizability of these models. The synergy between the systems was evident, exemplified by strong positive correlations, such as between MIRANDA’s Elasticity and RELAPP’s c_exp (parameter ‘c’ of its mathematical model m1, r = 0.858) and final resistive force (r = 0.839). Despite the limited sample size, these findings highlight MIRANDA’s versatility and speed for efficient material characterization. MIRANDA and RELAPP offer significant industrial implications for viscoelastic materials, including accelerating development cycles and enhancing continuous quality control. This approach has strong potential to reduce reliance on slower, traditional methods, warranting further validation with larger datasets.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/226038
url https://hdl.handle.net/2445/226038
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/ma18214827
Materials, 2025, vol. 18, p. 4827
https://doi.org/10.3390/ma18214827
dc.rights.none.fl_str_mv cc-by (c) Monleón-Getino,Antonio et al., 2025
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Monleón-Getino,Antonio et al., 2025
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 27 p.
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Genètica, Microbiologia i Estadística)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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