Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models
In this work, some characteristics of the black and whiteinks that are part of the graphic printing processes,calledrotogravure, were evaluated. In this process, the ink playsa decisive role in the quality of the material produced,therefore, its properties must be evaluated and guaranteedin order t...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | Portuguese |
| Published: |
UNESP
2023-07-01
|
| Series: | CQD Revista Eletrônica Paulista de Matemática |
| Subjects: | |
| Online Access: | https://sistemas.fc.unesp.br/ojs/index.php/revistacqd/article/view/359 |
| _version_ | 1797679847362789376 |
|---|---|
| author | Camila Proni Eduardo Hideki Oshiro Edenir Rodrigues Pereira-Filho Érica Regina Filletti |
| author_facet | Camila Proni Eduardo Hideki Oshiro Edenir Rodrigues Pereira-Filho Érica Regina Filletti |
| author_sort | Camila Proni |
| collection | DOAJ |
| description |
In this work, some characteristics of the black and whiteinks that are part of the graphic printing processes,calledrotogravure, were evaluated. In this process, the ink playsa decisive role in the quality of the material produced,therefore, its properties must be evaluated and guaranteedin order to obtain a product that works properly in theprinting process.Thus, an analytical method was devel-oped that combines infrared spectroscopy with ArtificialNeural Networks (ANN) to estimate the viscosity, densityand solids content of inks, having the advantage of provid-ing highly accurate results very quickly and with littlecomputational effort. The best models were those devel-oped for density, with average percentage errors of: 1% intraining and validation, and 2% in testof the black andwhite inks together; 1% in training and validation, and0.7% in test of theblack ink; 0.2% in training, 0.8% invalidation and 0.7% in test of white ink. The method de-veloped has the potential to be applied in printing indus-tries as an improvement for the production of high qualityrotogravure printed material
|
| first_indexed | 2024-03-11T23:20:52Z |
| format | Article |
| id | doaj.art-c0f6ee1c02aa43feb21040ccda066200 |
| institution | Directory Open Access Journal |
| issn | 2316-9664 |
| language | Portuguese |
| last_indexed | 2024-03-11T23:20:52Z |
| publishDate | 2023-07-01 |
| publisher | UNESP |
| record_format | Article |
| series | CQD Revista Eletrônica Paulista de Matemática |
| spelling | doaj.art-c0f6ee1c02aa43feb21040ccda0662002023-09-20T14:34:48ZporUNESPCQD Revista Eletrônica Paulista de Matemática2316-96642023-07-0123110.21167/cqdv23n12023082098Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural modelsCamila Proni0Eduardo Hideki Oshiro1Edenir Rodrigues Pereira-Filho 2Érica Regina Filletti3UNESP - Universidade Estadual Paulista “Júlio de Mesquita Filho"Senai “Fundação Zerrener“ UFSCAR - Universidade Federal de São CarlosUNESP - Universidade Estadual Paulista “Júlio de Mesquita Filho“ In this work, some characteristics of the black and whiteinks that are part of the graphic printing processes,calledrotogravure, were evaluated. In this process, the ink playsa decisive role in the quality of the material produced,therefore, its properties must be evaluated and guaranteedin order to obtain a product that works properly in theprinting process.Thus, an analytical method was devel-oped that combines infrared spectroscopy with ArtificialNeural Networks (ANN) to estimate the viscosity, densityand solids content of inks, having the advantage of provid-ing highly accurate results very quickly and with littlecomputational effort. The best models were those devel-oped for density, with average percentage errors of: 1% intraining and validation, and 2% in testof the black andwhite inks together; 1% in training and validation, and0.7% in test of theblack ink; 0.2% in training, 0.8% invalidation and 0.7% in test of white ink. The method de-veloped has the potential to be applied in printing indus-tries as an improvement for the production of high qualityrotogravure printed material https://sistemas.fc.unesp.br/ojs/index.php/revistacqd/article/view/359InksViscosityDensitySolids contentArtificial neural networks. |
| spellingShingle | Camila Proni Eduardo Hideki Oshiro Edenir Rodrigues Pereira-Filho Érica Regina Filletti Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models CQD Revista Eletrônica Paulista de Matemática Inks Viscosity Density Solids content Artificial neural networks. |
| title | Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| title_full | Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| title_fullStr | Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| title_full_unstemmed | Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| title_short | Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| title_sort | prediction of viscosity density and solids content in inks employed in printing industry production chain combining infrared and neural models |
| topic | Inks Viscosity Density Solids content Artificial neural networks. |
| url | https://sistemas.fc.unesp.br/ojs/index.php/revistacqd/article/view/359 |
| work_keys_str_mv | AT camilaproni predictionofviscositydensityandsolidscontentininksemployedinprintingindustryproductionchaincombininginfraredandneuralmodels AT eduardohidekioshiro predictionofviscositydensityandsolidscontentininksemployedinprintingindustryproductionchaincombininginfraredandneuralmodels AT edenirrodriguespereirafilho predictionofviscositydensityandsolidscontentininksemployedinprintingindustryproductionchaincombininginfraredandneuralmodels AT ericareginafilletti predictionofviscositydensityandsolidscontentininksemployedinprintingindustryproductionchaincombininginfraredandneuralmodels |