Publicação
AI-Powered Data Management to Optimize Data Collection and Processing in a Painting Laboratory
| dc.contributor.author | Pereira, Maria Teresa Ribeiro | |
| dc.contributor.author | Pereira, Marisa João Guerra | |
| dc.contributor.author | Tavares, Miguel Guedes | |
| dc.contributor.author | Guimarães, André | |
| dc.contributor.author | Vilarinho, Hermilio | |
| dc.date.accessioned | 2026-02-18T12:16:07Z | |
| dc.date.available | 2026-02-18T12:16:07Z | |
| dc.date.issued | 2026-02-10 | en_US |
| dc.date.updated | 2026-02-13T15:42:24Z | |
| dc.description.abstract | Industrial laboratories often remain under-digitized compared to production lines, creating a gap between data acquisition and analytical intelligence, critical for advanced quality control. This study addresses this gap by proposing and validating a novel framework that combines Low-Code digitalisation tools with Machine Learning (ML) and Causal Inference to optimise data collection and analysis in an automotive painting laboratory. A Microsoft Power Apps-based platform was developed in order to digitalise all measurement records, eliminating manual transcription errors (previously ≈ 40.01%) and reducing data-handling time by up to 34% of an operator’s shift, while enabling centralised, traceable storage and Power BI integration. Four datasets were used to assess predictive capacity with Random Forest, XGBoost and Neural Networks; Random Forest consistently provided the most stable results—Mean Absolute Error (MAE) of 0.972, Mean Absolute Percentage Error (MAPE) of 16.45%, and Root Mean Square Error (RMSE) of 1.307. Causal models (Linear Regression, DoWhy, Causal Forest, Double Machine Learning) consistently identified ultrafiltrate I solid content of the electrodeposition process as a dominant causal factor for defects. This study provides a novel framework that bridges digitalisation and ML-based causal reasoning in laboratory settings, offering a scalable approach that can be extended and replicated in other industrial sectors, aiming to develop smart, data-driven quality control systems. | eng |
| dc.description.version | info:eu-repo/semantics/publishedVersion | |
| dc.identifier.citation | Pereira, M. T. R.; Guerra Pereira, M. J.; Tavares, M. G.; Guimarães, A. M.; Vilarinho, H. AI-Powered Data Management to Optimize Data Collection and Processing in a Painting Laboratory. Journal of Mechanical Engineering and Manufacturing 2026. https://doi.org/10.53941/jmem.2026.100011. | |
| dc.identifier.doi | 10.53941/jmem.2026.100011 | en_US |
| dc.identifier.slug | cv-prod-4734359 | |
| dc.identifier.uri | http://hdl.handle.net/10400.19/9693 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | digitalisation | |
| dc.subject | laboratory | |
| dc.subject | electrodeposition | |
| dc.subject | machine learning | |
| dc.subject | causal inference | |
| dc.title | AI-Powered Data Management to Optimize Data Collection and Processing in a Painting Laboratory | en_US |
| dc.type | research article | en_US |
| dspace.entity.type | Publication | |
| oaire.citation.title | Journal of Mechanical Engineering and Manufacturing | en_US |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Guimarães | |
| person.givenName | André | |
| person.identifier.ciencia-id | AE12-CE94-82B0 | |
| person.identifier.orcid | 0000-0001-6346-5719 | |
| rcaap.cv.cienciaid | AE12-CE94-82B0 | André Guimarães | |
| rcaap.rights | openAccess | en_US |
| relation.isAuthorOfPublication | 6d7402ff-c1c5-44bd-845c-a8300b11da5c | |
| relation.isAuthorOfPublication.latestForDiscovery | 6d7402ff-c1c5-44bd-845c-a8300b11da5c |